Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding (2312.16834v1)

Published 28 Dec 2023 in cs.LG, cs.AI, and cs.SI

Abstract: We investigate the problem of multiplex graph embedding, that is, graphs in which nodes interact through multiple types of relations (dimensions). In recent years, several methods have been developed to address this problem. However, the need for more effective and specialized approaches grows with the production of graph data with diverse characteristics. In particular, real-world multiplex graphs may exhibit a high number of dimensions, making it difficult to construct a single consensus representation. Furthermore, important information can be hidden in complex latent structures scattered in multiple dimensions. To address these issues, we propose HMGE, a novel embedding method based on hierarchical aggregation for high-dimensional multiplex graphs. Hierarchical aggregation consists of learning a hierarchical combination of the graph dimensions and refining the embeddings at each hierarchy level. Non-linear combinations are computed from previous ones, thus uncovering complex information and latent structures hidden in the multiplex graph dimensions. Moreover, we leverage mutual information maximization between local patches and global summaries to train the model without supervision. This allows to capture of globally relevant information present in diverse locations of the graph. Detailed experiments on synthetic and real-world data illustrate the suitability of our approach to downstream supervised tasks, including link prediction and node classification.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (39)
  1. M. Kivelä, A. Arenas, M. Barthelemy, J. P. Gleeson, Y. Moreno, and M. A. Porter, “Multilayer networks,” Journal of Complex Networks, vol. 2, no. 3, pp. 203–271, 2014.
  2. R. Oughtred, J. Rust, C. Chang, B.-J. Breitkreutz, C. Stark, A. Willems, L. Boucher, G. Leung, N. Kolas, F. Zhang et al., “The biogrid database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions,” Protein Science, vol. 30, no. 1, pp. 187–200, 2021.
  3. X. Zhang, L. He, K. Chen, Y. Luo, J. Zhou, and F. Wang, “Multi-view graph convolutional network and its applications on neuroimage analysis for parkinson’s disease,” in AMIA Annual Symposium Proceedings, vol. 2018.   American Medical Informatics Association, 2018, pp. 1147–1156.
  4. M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale, and D. Pedreschi, “Multidimensional networks: foundations of structural analysis,” World Wide Web, vol. 16, no. 5, pp. 567–593, 2013.
  5. J. Sun, Y. Zhang, C. Ma, M. Coates, H. Guo, R. Tang, and X. He, “Multi-graph convolution collaborative filtering,” in International Conference on Data Mining.   IEEE, 2019, pp. 1306–1311.
  6. R. J. Sánchez-García, E. Cozzo, and Y. Moreno, “Dimensionality reduction and spectral properties of multilayer networks,” Physical Review E, vol. 89, no. 5, p. 052815, 2014.
  7. M. De Domenico, V. Nicosia, A. Arenas, and V. Latora, “Structural reducibility of multilayer networks,” Nature Communications, vol. 6, no. 1, pp. 1–9, 2015.
  8. A. Solé-Ribalta, M. De Domenico, S. Gómez, and A. Arenas, “Random walk centrality in interconnected multilayer networks,” Physica D: Nonlinear Phenomena, vol. 323, pp. 73–79, 2016.
  9. M. El Gheche, G. Chierchia, and P. Frossard, “Orthonet: multilayer network data clustering,” IEEE Transactions on Signal and Information Processing over Networks, vol. 6, pp. 152–162, 2020.
  10. S. Fan, X. Wang, C. Shi, E. Lu, K. Lin, and B. Wang, “One2multi graph autoencoder for multi-view graph clustering,” in The Web Conference, 2020, pp. 3070–3076.
  11. C. Park, D. Kim, J. Han, and H. Yu, “Unsupervised attributed multiplex network embedding,” in AAAI Conference on Artificial Intelligence, vol. 34, no. 04, 2020, pp. 5371–5378.
  12. B. Jing, C. Park, and H. Tong, “Hdmi: High-order deep multiplex infomax,” in The Web Conference, 2021, pp. 2414–2424.
  13. X. Chu, X. Fan, D. Yao, Z. Zhu, J. Huang, and J. Bi, “Cross-network embedding for multi-network alignment,” in The Web Conference, 2019, pp. 273–284.
  14. L. Pio-Lopez, A. Valdeolivas, L. Tichit, É. Remy, and A. Baudot, “Multiverse: a multiplex and multiplex-heterogeneous network embedding approach,” Scientific Reports, vol. 11, no. 1, pp. 1–20, 2021.
  15. H. Zhang, L. Qiu, L. Yi, and Y. Song, “Scalable multiplex network embedding.” in International Joint Conferences on Artificial Intelligence, vol. 18, 2018, pp. 3082–3088.
  16. W. Liu, P.-Y. Chen, S. Yeung, T. Suzumura, and L. Chen, “Principled multilayer network embedding,” in International Conference on Data Mining Workshops.   IEEE, 2017, pp. 134–141.
  17. B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in International Conference on Knowledge Discovery and Data Mining, 2014, pp. 701–710.
  18. L. Xu, X. Wei, J. Cao, and P. S. Yu, “Multi-task network embedding,” International Journal of Data Science and Analytics, vol. 8, no. 2, pp. 183–198, 2019.
  19. J. Ni, S. Chang, X. Liu, W. Cheng, H. Chen, D. Xu, and X. Zhang, “Co-regularized deep multi-network embedding,” in The Web Conference, 2018, pp. 469–478.
  20. R. Matsuno and T. Murata, “Mell: effective embedding method for multiplex networks,” in The Web Conference, 2018, pp. 1261–1268.
  21. Y. Ma, S. Wang, C. C. Aggarwal, D. Yin, and J. Tang, “Multi-dimensional graph convolutional networks,” in International Conference on Data Mining.   SIAM, 2019, pp. 657–665.
  22. Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 4–24, 2020.
  23. Z. Zhang, P. Cui, and W. Zhu, “Deep learning on graphs: A survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 1, pp. 249–270, 2022.
  24. O. Boutemine and M. Bouguessa, “Mining community structures in multidimensional networks,” ACM Transactions on Knowledge Discovery from Data, vol. 11, no. 4, pp. 1–36, 2017.
  25. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
  26. J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai et al., “Recent advances in convolutional neural networks,” Pattern Recognition, vol. 77, pp. 354–377, 2018.
  27. P. Cui, X. Wang, J. Pei, and W. Zhu, “A survey on network embedding,” IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 5, pp. 833–852, 2018.
  28. A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in International Conference on Knowledge Discovery and Data Mining, 2016, pp. 855–864.
  29. Y. Cen, X. Zou, J. Zhang, H. Yang, J. Zhou, and J. Tang, “Representation learning for attributed multiplex heterogeneous network,” in International Conference on Knowledge Discovery and Data Mining, 2019, pp. 1358–1368.
  30. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” Advances in Neural Information Processing Systems, vol. 26, p. 3111–3119, 2013.
  31. J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “Line: Large-scale information network embedding,” in The Web Conference, 2015, pp. 1067–1077.
  32. R. D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler, and Y. Bengio, “Learning deep representations by mutual information estimation and maximization,” in International Conference on Learning Representations, 2019.
  33. A. Mitra, P. Vijayan, R. Sanasam, D. Goswami, S. Parthasarathy, and B. Ravindran, “Semi-supervised deep learning for multiplex networks,” in International Conference on Knowledge Discovery and Data Mining, 2021, pp. 1234–1244.
  34. D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” International Conference On Learning Representations, 2015.
  35. Y. Liu, M. Jin, S. Pan, C. Zhou, Y. Zheng, F. Xia, and P. Yu, “Graph self-supervised learning: A survey,” IEEE Transactions on Knowledge and Data Engineering, 2022.
  36. P. Velickovic, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm, “Deep graph infomax,” International Conference on Learning Representations, 2019.
  37. J. Tang, “Aminer: Toward understanding big scholar data,” in International Conference on Web Search and Data Mining, 2016, pp. 467–467.
  38. D. Szklarczyk, A. L. Gable, D. Lyon, A. Junge, S. Wyder, J. Huerta-Cepas, M. Simonovic, N. T. Doncheva, J. H. Morris, P. Bork et al., “String v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets,” Nucleic Acids Research, vol. 47, no. 1, pp. 607–613, 2019.
  39. P. W. Holland, K. B. Laskey, and S. Leinhardt, “Stochastic blockmodels: First steps,” Social Networks, vol. 5, no. 2, pp. 109–137, 1983.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now