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BERT4FCA: A Method for Bipartite Link Prediction using Formal Concept Analysis and BERT (2402.08236v1)
Published 13 Feb 2024 in cs.AI
Abstract: We propose BERT4FCA, a novel method for link prediction in bipartite networks, using formal concept analysis (FCA) and BERT. Link prediction in bipartite networks is an important task that can solve various practical problems like friend recommendation in social networks and co-authorship prediction in author-paper networks. Recent research has found that in bipartite networks, maximal bi-cliques provide important information for link prediction, and they can be extracted by FCA. Some FCA-based bipartite link prediction methods have achieved good performance. However, we figured out that their performance could be further improved because these methods did not fully capture the rich information of the extracted maximal bi-cliques. To address this limitation, we propose an approach using BERT, which can learn more information from the maximal bi-cliques extracted by FCA and use them to make link prediction. We conduct experiments on three real-world bipartite networks and demonstrate that our method outperforms previous FCA-based methods, and some classic methods such as matrix-factorization and node2vec.
- Getoor, L., Diehl, C.P.: Link mining: a survey. Acm Sigkdd Explorations Newsletter 7(2), 3–12 (2005) Martínez et al. [2016] Martínez, V., Berzal, F., Cubero, J.: A survey of link prediction in complex networks. ACM computing surveys (CSUR) 49(4), 1–33 (2016) Wang et al. [2014] Wang, P., Xu, B., Wu, Y., Zhou, X.: Link prediction in social networks: the state-of-the-art. arXiv preprint arXiv:1411.5118 (2014) Shang et al. [2010] Shang, M., Lü, L., Zhang, Y., Zhou, T.: Empirical analysis of web-based user-object bipartite networks. Europhysics Letters 90(4), 48006 (2010) Asratian et al. [1998] Asratian, A.S., Denley, T.M., Häggkvist, R.: Bipartite Graphs and Their Applications vol. 131, (1998) Chen et al. [2017] Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Martínez, V., Berzal, F., Cubero, J.: A survey of link prediction in complex networks. ACM computing surveys (CSUR) 49(4), 1–33 (2016) Wang et al. [2014] Wang, P., Xu, B., Wu, Y., Zhou, X.: Link prediction in social networks: the state-of-the-art. arXiv preprint arXiv:1411.5118 (2014) Shang et al. [2010] Shang, M., Lü, L., Zhang, Y., Zhou, T.: Empirical analysis of web-based user-object bipartite networks. Europhysics Letters 90(4), 48006 (2010) Asratian et al. [1998] Asratian, A.S., Denley, T.M., Häggkvist, R.: Bipartite Graphs and Their Applications vol. 131, (1998) Chen et al. [2017] Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Wang, P., Xu, B., Wu, Y., Zhou, X.: Link prediction in social networks: the state-of-the-art. arXiv preprint arXiv:1411.5118 (2014) Shang et al. [2010] Shang, M., Lü, L., Zhang, Y., Zhou, T.: Empirical analysis of web-based user-object bipartite networks. Europhysics Letters 90(4), 48006 (2010) Asratian et al. [1998] Asratian, A.S., Denley, T.M., Häggkvist, R.: Bipartite Graphs and Their Applications vol. 131, (1998) Chen et al. [2017] Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, M., Lü, L., Zhang, Y., Zhou, T.: Empirical analysis of web-based user-object bipartite networks. Europhysics Letters 90(4), 48006 (2010) Asratian et al. [1998] Asratian, A.S., Denley, T.M., Häggkvist, R.: Bipartite Graphs and Their Applications vol. 131, (1998) Chen et al. [2017] Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Asratian, A.S., Denley, T.M., Häggkvist, R.: Bipartite Graphs and Their Applications vol. 131, (1998) Chen et al. [2017] Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. 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In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. 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Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
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Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. 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[2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Wang, P., Xu, B., Wu, Y., Zhou, X.: Link prediction in social networks: the state-of-the-art. arXiv preprint arXiv:1411.5118 (2014) Shang et al. [2010] Shang, M., Lü, L., Zhang, Y., Zhou, T.: Empirical analysis of web-based user-object bipartite networks. Europhysics Letters 90(4), 48006 (2010) Asratian et al. [1998] Asratian, A.S., Denley, T.M., Häggkvist, R.: Bipartite Graphs and Their Applications vol. 131, (1998) Chen et al. [2017] Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, M., Lü, L., Zhang, Y., Zhou, T.: Empirical analysis of web-based user-object bipartite networks. Europhysics Letters 90(4), 48006 (2010) Asratian et al. [1998] Asratian, A.S., Denley, T.M., Häggkvist, R.: Bipartite Graphs and Their Applications vol. 131, (1998) Chen et al. [2017] Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Asratian, A.S., Denley, T.M., Häggkvist, R.: Bipartite Graphs and Their Applications vol. 131, (1998) Chen et al. [2017] Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. 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[2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. 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In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, M., Lü, L., Zhang, Y., Zhou, T.: Empirical analysis of web-based user-object bipartite networks. Europhysics Letters 90(4), 48006 (2010) Asratian et al. [1998] Asratian, A.S., Denley, T.M., Häggkvist, R.: Bipartite Graphs and Their Applications vol. 131, (1998) Chen et al. [2017] Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Asratian, A.S., Denley, T.M., Häggkvist, R.: Bipartite Graphs and Their Applications vol. 131, (1998) Chen et al. [2017] Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Shang, M., Lü, L., Zhang, Y., Zhou, T.: Empirical analysis of web-based user-object bipartite networks. Europhysics Letters 90(4), 48006 (2010) Asratian et al. [1998] Asratian, A.S., Denley, T.M., Häggkvist, R.: Bipartite Graphs and Their Applications vol. 131, (1998) Chen et al. [2017] Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Asratian, A.S., Denley, T.M., Häggkvist, R.: Bipartite Graphs and Their Applications vol. 131, (1998) Chen et al. [2017] Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. 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Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Chen, B., Li, F., Chen, S., Hu, R., Chen, L.: Link prediction based on non-negative matrix factorization. PloS one 12(8), 0182968 (2017) Dürrschnabel et al. [2021] Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Dürrschnabel, D., Hanika, T., Stubbemann, M.: Fca2vec: Embedding techniques for formal concept analysis. In: Complex Data Analytics with Formal Concept Analysis, pp. 47–74. Springer, Cham (2021) Menon and Elkan [2011a] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. 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[2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. 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[2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Menon and Elkan [2011b] Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. 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[2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. 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[2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II 22, pp. 437–452 (2011). Springer Fokoue et al. [2016] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. In: The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Proceedings 13, pp. 774–789 (2016). Springer Kunegis et al. [2010] Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. 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[2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. 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In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. 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Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 380–389 (2010). Springer Lü and Zhou [2011] Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. 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Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390(6), 1150–1170 (2011) Grover and Leskovec [2016] Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
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IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016) Ma et al. [2016] Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ma, C., Zhou, T., Zhang, H.: Playing the role of weak clique property in link prediction: A friend recommendation model. Scientific Reports 6(1), 30098 (2016) Zhao et al. [2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
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[2019] Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Zhao, J., Sun, M., Chen, F., Chiu, P.: Missbin: Visual analysis of missing links in bipartite networks. In: 2019 IEEE Visualization Conference (VIS), pp. 71–75 (2019). IEEE Xia et al. [2012] Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. 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[2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. 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Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. 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[2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. 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[2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Xia, S., Dai, B., Lim, E., Zhang, Y., Xing, C.: Link prediction for bipartite social networks: The role of structural holes. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 153–157 (2012). IEEE Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. 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[2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
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[2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Marquer et al. [2020] Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
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Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Marquer, E., Kulkarni, A., Couceiro, M.: Embedding formal contexts using unordered composition. In: FCA4AI-8th International Workshop “What Can FCA do for Artificial Intelligence?” (co-located with ECAI2020) (2020) Gaume et al. [2013] Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Gaume, B., Navarro, E., Prade, H.: Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts. International Journal of Computational Intelligence Systems 6(6), 1125–1142 (2013) Mikolov et al. [2013a] Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
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[2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013) Mikolov et al. [2013b] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. 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Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Graves and Schmidhuber [2005] Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. 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Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. 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Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
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Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. 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[2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
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In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural networks 18(5-6), 602–610 (2005) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Bernhard Ganter [1999] Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
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[2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. 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Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. 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In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
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Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Bernhard Ganter, R.W.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer, Berlin Heidelberg (1999) Ignatov [2015] Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. Information Retrieval: 8th Russian Summer School, RuSSIR 2014, Nizhniy Novgorod, Russia, August 18-22, 2014, Revised Selected Papers 8, 42–141 (2015) Poelmans et al. [2013a] Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert systems with applications 40(16), 6601–6623 (2013) Poelmans et al. [2013b] Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications 40(16), 6538–6560 (2013) Devlin et al. [2018] Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) Estmark [2021] Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Estmark, A.: Text Block Prediction and Article Reconstruction Using BERT (2021) Lazega [1995] Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Lazega, E.: Structural holes: the social structure of competition. JSTOR (1995) Peng et al. [2023] Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Peng, S., Yamamoto, A., Ito, K.: Link prediction on bipartite networks using matrix factorization with negative sample selection. Plos one 18(8), 0289568 (2023) Kingma and Welling [2013] Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013) Peng and Yamamoto [2023] Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Peng, S., Yamamoto, A.: Z-tca: Fast algorithm for triadic concept analysis using zero-suppressed decision diagrams. Journal of Information Processing 31, 722–733 (2023) Shang et al. [2016] Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. In: International Conference on Machine Learning, pp. 2217–2225 (2016). PMLR Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006) Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
- Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)