Towards Causal Classification: A Comprehensive Study on Graph Neural Networks (2401.15444v1)
Abstract: The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approximation capabilities. Anticipated to significantly enhance common graph-based tasks such as classification and prediction, the development of a causally enhanced GNN framework is yet to be thoroughly investigated. Addressing this shortfall, our study delves into nine benchmark graph classification models, testing their strength and versatility across seven datasets spanning three varied domains to discern the impact of causality on the predictive prowess of GNNs. This research offers a detailed assessment of these models, shedding light on their efficiency, and flexibility in different data environments, and highlighting areas needing advancement. Our findings are instrumental in furthering the understanding and practical application of GNNs in diverse datacentric fields
- M. Xie, M. Irfan, A. Razzaq, and V. Dagar, “Forest and mineral volatility and economic performance: evidence from frequency domain causality approach for global data,” Resources Policy, vol. 76, p. 102685, 2022.
- M. Ridley, G. Rao, F. Schilbach, and V. Patel, “Poverty, depression, and anxiety: Causal evidence and mechanisms,” Science, vol. 370, no. 6522, p. eaay0214, 2020.
- M. C. Brouwers, N. Simons, C. D. Stehouwer, and A. Isaacs, “Non-alcoholic fatty liver disease and cardiovascular disease: assessing the evidence for causality,” Diabetologia, vol. 63, pp. 253–260, 2020.
- M. Prosperi, Y. Guo, M. Sperrin, J. S. Koopman, J. S. Min, X. He, S. Rich, M. Wang, I. E. Buchan, and J. Bian, “Causal inference and counterfactual prediction in machine learning for actionable healthcare,” Nature Machine Intelligence, vol. 2, no. 7, pp. 369–375, 2020.
- T. S. Adebayo, “Environmental consequences of fossil fuel in spain amidst renewable energy consumption: a new insights from the wavelet-based granger causality approach,” International Journal of Sustainable Development & World Ecology, vol. 29, no. 7, pp. 579–592, 2022.
- W. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. Tang, and D. Yin, “Graph neural networks for social recommendation,” in The world wide web conference, 2019, pp. 417–426.
- J. Chang, C. Gao, Y. Zheng, Y. Hui, Y. Niu, Y. Song, D. Jin, and Y. Li, “Sequential recommendation with graph neural networks,” in Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, 2021, pp. 378–387.
- J. Li, T. Zhang, H. Tian, S. Jin, M. Fardad, and R. Zafarani, “Graph sparsification with graph convolutional networks,” International Journal of Data Science and Analytics, pp. 1–14, 2022.
- M. A. Gharsallaoui, F. Tornaci, and I. Rekik, “Investigating and quantifying the reproducibility of graph neural networks in predictive medicine,” in Predictive Intelligence in Medicine: 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings 4. Springer, 2021, pp. 104–116.
- A. Sharma, P. K. Singh, P. Nikashina, V. Gavrilenko, A. Tselykh, and A. Bozhenyuk, “Ai and gnn model for predictive analytics on patient data and its usefulness in digital healthcare technologies,” in IoT, Big Data and AI for Improving Quality of Everyday Life: Present and Future Challenges: IOT, Data Science and Artificial Intelligence Technologies. Springer, 2023, pp. 331–345.
- L. Luo, Y. Fang, X. Cao, X. Zhang, and W. Zhang, “Detecting communities from heterogeneous graphs: A context path-based graph neural network model,” in Proceedings of the 30th ACM international conference on information & knowledge management, 2021, pp. 1170–1180.
- P. Li, H. Yu, X. Luo, and J. Wu, “Lgm-gnn: A local and global aware memory-based graph neural network for fraud detection,” IEEE Transactions on Big Data, 2023.
- M. Zečević, D. S. Dhami, P. Veličković, and K. Kersting, “Relating graph neural networks to structural causal models,” arXiv preprint arXiv:2109.04173, 2021.
- V. P. Dwivedi, C. K. Joshi, A. T. Luu, T. Laurent, Y. Bengio, and X. Bresson, “Benchmarking graph neural networks,” arXiv preprint arXiv:2003.00982, 2020.
- T. Li, Z. Zhou, S. Li, C. Sun, R. Yan, and X. Chen, “The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study,” Mechanical Systems and Signal Processing, vol. 168, p. 108653, 2022.
- M. Kosan, S. Verma, B. Armgaan, K. Pahwa, A. Singh, S. Medya, and S. Ranu, “Gnnx-bench: Unravelling the utility of perturbation-based gnn explainers through in-depth benchmarking,” arXiv preprint arXiv:2310.01794, 2023.
- T. Chen, K. Zhou, K. Duan, W. Zheng, P. Wang, X. Hu, and Z. Wang, “Bag of tricks for training deeper graph neural networks: A comprehensive benchmark study,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 2769–2781, 2022.
- T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv.org, 2017.
- W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” Advances in neural information processing systems, vol. 30, 2017.
- P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” arXiv.org, 2018.
- K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” arXiv preprint arXiv:1810.00826, 2018.
- J. You, R. Ying, and J. Leskovec, “Position-aware graph neural networks,” in International conference on machine learning. PMLR, 2019, pp. 7134–7143.
- T. Zhao, Y. Liu, L. Neves, O. Woodford, M. Jiang, and N. Shah, “Data augmentation for graph neural networks,” in Proceedings of the aaai conference on artificial intelligence, vol. 35, no. 12, 2021, pp. 11 015–11 023.
- J. Zhang, X. Shi, S. Zhao, and I. King, “Star-gcn: Stacked and reconstructed graph convolutional networks for recommender systems,” arXiv preprint arXiv:1905.13129, 2019.
- L. Zhao, Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, and H. Li, “T-gcn: A temporal graph convolutional network for traffic prediction,” IEEE transactions on intelligent transportation systems, vol. 21, no. 9, pp. 3848–3858, 2019.
- J. Song, J. Son, D.-h. Seo, K. Han, N. Kim, and S.-W. Kim, “St-gat: A spatio-temporal graph attention network for accurate traffic speed prediction,” in Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022, pp. 4500–4504.
- S. Wang, X. Su, B. Zhao, P. Hu, T. Bai, and L. Hu, “An improved graph isomorphism network for accurate prediction of drug–drug interactions,” Mathematics, vol. 11, no. 18, p. 3990, 2023.
- J. Wu, J. He, and J. Xu, “Net: Degree-specific graph neural networks for node and graph classification,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 406–415.
- S. K. Maurya, X. Liu, and T. Murata, “Simplifying approach to node classification in graph neural networks,” Journal of Computational Science, vol. 62, p. 101695, 2022.
- K. Wang, J. An, M. Zhou, Z. Shi, X. Shi, and Q. Kang, “Minority-weighted graph neural network for imbalanced node classification in social networks of internet of people,” IEEE Internet of Things Journal, vol. 10, no. 1, pp. 330–340, 2022.
- Z. Sun, W. Zhang, L. Mou, Q. Zhu, Y. Xiong, and L. Zhang, “Generalized equivariance and preferential labeling for gnn node classification,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 8, 2022, pp. 8395–8403.
- Z. Wang, Z. Lin, S. Li, Y. Wang, W. Zhong, X. Wang, and J. Xin, “Dynamic multi-task graph isomorphism network for classification of alzheimer’s disease,” Applied Sciences, vol. 13, no. 14, p. 8433, 2023.
- K. Yu, X. Guo, L. Liu, J. Li, H. Wang, Z. Ling, and X. Wu, “Causality-based feature selection: Methods and evaluations,” ACM Computing Surveys (CSUR), vol. 53, no. 5, pp. 1–36, 2020.
- B. Schölkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio, “Toward causal representation learning,” Proceedings of the IEEE, vol. 109, no. 5, pp. 612–634, 2021.
- Y. Sui, X. Wang, J. Wu, M. Lin, X. He, and T.-S. Chua, “Causal attention for interpretable and generalizable graph classification,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 1696–1705.
- H. Wang, R. Liu, S. X. Ding, Q. Hu, Z. Li, and H. Zhou, “Causal-trivial attention graph neural network for fault diagnosis of complex industrial processes,” IEEE Transactions on Industrial Informatics, 2023.
- F. Ding, X. Zhang, J. Sybrandt, and I. Safro, “Unsupervised hierarchical graph representation learning by mutual information maximization,” arXiv preprint arXiv:2003.08420, 2020.
- X. Di, P. Yu, R. Bu, and M. Sun, “Mutual information maximization in graph neural networks,” in 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020, pp. 1–7.
- N. Wale, I. A. Watson, and G. Karypis, “Comparison of descriptor spaces for chemical compound retrieval and classification,” Knowledge and Information Systems, vol. 14, pp. 347–375, 2008.
- K. M. Borgwardt, C. S. Ong, S. Schönauer, S. Vishwanathan, A. J. Smola, and H.-P. Kriegel, “Protein function prediction via graph kernels,” Bioinformatics, vol. 21, no. suppl_1, pp. i47–i56, 2005.
- A. K. Debnath, R. L. Lopez de Compadre, G. Debnath, A. J. Shusterman, and C. Hansch, “Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity,” Journal of medicinal chemistry, vol. 34, no. 2, pp. 786–797, 1991.
- Y. Zhou, H. Huo, Z. Hou, and F. Bu, “A deep graph convolutional neural network architecture for graph classification,” Plos one, vol. 18, no. 3, p. e0279604, 2023.
- Y. Xie, S. Lv, Y. Qian, C. Wen, and J. Liang, “Active and semi-supervised graph neural networks for graph classification,” IEEE Transactions on Big Data, vol. 8, no. 4, pp. 920–932, 2022.
- A. K. McCallum, K. Nigam, J. Rennie, and K. Seymore, “Automating the construction of internet portals with machine learning,” Information retrieval (Boston), vol. 3, no. 1, pp. 127–, 2000.
- C. L. Giles, K. D. Bollacker, and S. Lawrence, “Citeseer: An automatic citation indexing system,” in Proceedings of the Third ACM Conference on Digital Libraries, ser. DL ’98. New York, NY, USA: Association for Computing Machinery, 1998, p. 89–98.
- P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” arXiv preprint arXiv:1710.10903, 2017.
- T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907, 2016.
- P. Yanardag and S. Vishwanathan, “Deep graph kernels,” in Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 2015, pp. 1365–1374.
- P. Zhou, Z. Wu, G. Wen, K. Tang, and J. Ma, “Multi-scale graph classification with shared graph neural network,” World Wide Web, vol. 26, no. 3, pp. 949–966, 2023.
- H.-J. Moon and S.-B. Cho, “A subgraph embedded gin with attention for graph classification,” in International Conference on Intelligent Data Engineering and Automated Learning. Springer, 2023, pp. 356–367.