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A Causal Disentangled Multi-granularity Graph Classification Method

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Rough Sets (IJCRS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14481))

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Abstract

Graph data widely exists in real life, with large amounts of data and complex structures. It is necessary to map graph data to low-dimensional embedding. Graph classification, a critical graph task, mainly relies on identifying the important substructures within the graph. At present, some graph classification methods do not combine the multi-granularity characteristics of graph data. This lack of granularity distinction in modeling leads to a conflation of key information and false correlations within the model. So, achieving the desired goal of a credible and interpretable model becomes challenging. This paper proposes a causal disentangled multi-granularity graph representation learning method (CDM-GNN) to solve this challenge. The CDM-GNN model disentangles the important substructures and bias parts within the graph from a multi-granularity perspective. The disentanglement of the CDM-GNN model reveals important and bias parts, forming the foundation for its classification task, specifically, model interpretations. The CDM-GNN model exhibits strong classification performance and generates explanatory outcomes aligning with human cognitive patterns. In order to verify the effectiveness of the model, this paper compares the three real-world datasets MUTAG, PTC, and IMDM-M. Six state-of-the-art models, namely GCN, GAT, Top-k, ASAPool, SUGAR, and SAT are employed for comparison purposes. Additionally, a qualitative analysis of the interpretation results is conducted.

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References

  1. Bengio, Y., Courville, A.C., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)

    Article  Google Scholar 

  2. Chen, D., O’Bray, L., Borgwardt, K.M.: Structure-aware transformer for graph representation learning. In: Chaudhuri, K., Jegelka, S., Song, L., Szepesvári, C., Niu, G., Sabato, S. (eds.) International Conference on Machine Learning, ICML 2022, 17–23 July 2022, Baltimore, Maryland, USA. Proceedings of Machine Learning Research, vol. 162, pp. 3469–3489. PMLR (2022)

    Google Scholar 

  3. Cucala, D.J.T., Grau, B.C., Kostylev, E.V., Motik, B.: Explainable GNN-based models over knowledge graphs. In: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25–29, 2022. OpenReview.net (2022)

    Google Scholar 

  4. Debnath, A.K., Lopez de Compadre, R.L., Debnath, G., Shusterman, A.J., Hansch, C.: Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity. J. Med. Chem. 34(2), 786–797 (1991)

    Google Scholar 

  5. Fang, Y., et al.: Knowledge graph-enhanced molecular contrastive learning with functional prompt. Nat. Mach. Intell. 1–12 (2023)

    Google Scholar 

  6. Feng, A., You, C., Wang, S., Tassiulas, L.: Kergnns: Interpretable graph neural networks with graph kernels. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pp. 6614–6622. AAAI Press (2022)

    Google Scholar 

  7. Gao, H., Ji, S.: Graph u-nets. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9–15 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research, vol. 97, pp. 2083–2092. PMLR (2019)

    Google Scholar 

  8. Hendrycks, D., Dietterich, T.G.: Benchmarking neural network robustness to common corruptions and perturbations. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6–9, 2019. OpenReview.net (2019)

    Google Scholar 

  9. Hsieh, J., Liu, B., Huang, D., Fei-Fei, L., Niebles, J.C.: Learning to decompose and disentangle representations for video prediction. In: Bengio, S., Wallach, H.M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3–8, 2018, Montréal, Canada, pp. 515–524 (2018)

    Google Scholar 

  10. Hu, W., et al.: Open graph benchmark: Datasets for machine learning on graphs. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6–12, 2020, virtual (2020)

    Google Scholar 

  11. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24–26, 2017, Conference Track Proceedings. OpenReview.net (2017)

    Google Scholar 

  12. Liu, Y., Wang, X., Wu, S., Xiao, Z.: Independence promoted graph disentangled networks. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7–12, 2020, pp. 4916–4923. AAAI Press (2020)

    Google Scholar 

  13. Lucic, A., Ter Hoeve, M.A., Tolomei, G., De Rijke, M., Silvestri, F.: Cf-gnnexplainer: counterfactual explanations for graph neural networks. In: Camps-Valls, G., Ruiz, F.J.R., Valera, I. (eds.) International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 28–30 March 2022, Virtual Event. Proceedings of Machine Learning Research, vol. 151, pp. 4499–4511. PMLR (2022)

    Google Scholar 

  14. Luo, D., et al.: Parameterized explainer for graph neural network. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6–12, 2020, virtual (2020)

    Google Scholar 

  15. Ma, J., Cui, P., Kuang, K., Wang, X., Zhu, W.: Disentangled graph convolutional networks. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9–15 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research, vol. 97, pp. 4212–4221. PMLR (2019)

    Google Scholar 

  16. Ranjan, E., Sanyal, S., Talukdar, P.P.: ASAP: adaptive structure aware pooling for learning hierarchical graph representations. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7–12, 2020, pp. 5470–5477. AAAI Press (2020)

    Google Scholar 

  17. Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: Bonet, B., Koenig, S. (eds.) Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25–30, 2015, Austin, Texas, USA, pp. 4292–4293. AAAI Press (2015)

    Google Scholar 

  18. Sun, Q., et al.: SUGAR: subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism. In: Leskovec, J., Grobelnik, M., Najork, M., Tang, J., Zia, L. (eds.) WWW ’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19–23, 2021, pp. 2081–2091. ACM / IW3C2 (2021)

    Google Scholar 

  19. Toivonen, H., Srinivasan, A., King, R.D., Kramer, S., Helma, C.: Statistical evaluation of the predictive toxicology challenge 2000–2001. Bioinformatics 19(10), 1183–1193 (2003)

    Article  Google Scholar 

  20. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net (2018)

    Google Scholar 

  21. Wang, G.: DGCC: data-driven granular cognitive computing. Granular Comput. 2(4), 343–355 (2017)

    Article  Google Scholar 

  22. Wang, X., Jin, H., Zhang, A., He, X., Xu, T., Chua, T.: Disentangled graph collaborative filtering. In: Huang, J.X., Chang, Y., Cheng, X., Kamps, J., Murdock, V., Wen, J., Liu, Y. (eds.) Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25–30, 2020, pp. 1001–1010. ACM (2020)

    Google Scholar 

  23. Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: Gnnexplainer: generating explanations for graph neural networks. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8–14, 2019, Vancouver, BC, Canada, pp. 9240–9251 (2019)

    Google Scholar 

  24. Zhang, S., Rao, X., Tay, Y., Zhang, C.: Knowledge router: Learning disentangled representations for knowledge graphs. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1–10 (2021)

    Google Scholar 

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Nos. 62221005, 61936001, 61806031), Natural Science Foundation of Chongqing, China (Nos. cstc2019jcyj-cxttX0002, cstc2021ycjh-bgzxm0013), Project of Chongqing Municipal Education Commission, China (No. HZ2021008), and Doctoral Innovation Talent Program of Chongqing University of Posts and Telecommunications, China (Nos. BYJS202108, BYJS202209, BYJS202118).

Author information

Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing, China

    Yuan Li, Li Liu, Penggang Chen, Youmin Zhang & Guoyin Wang

  2. Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, 400065, The People’s Republic of China

    Yuan Li, Li Liu, Penggang Chen, Youmin Zhang & Guoyin Wang

Authors
  1. Yuan Li
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  2. Li Liu
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  3. Penggang Chen
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  4. Youmin Zhang
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  5. Guoyin Wang
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Corresponding author

Correspondence to Guoyin Wang .

Editor information

Editors and Affiliations

  1. IRCCS Istituto Ortopedico Galeazzi, Milano, Italy

    Andrea Campagner

  2. Ghent University, Ghent, Belgium

    Oliver Urs Lenz

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Shuyin Xia

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. AGH University of Science and Technology, Kraków, Poland

    Jarosław Wąs

  6. University of Regina, Regina, SK, Canada

    JingTao Yao

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Li, Y., Liu, L., Chen, P., Zhang, Y., Wang, G. (2023). A Causal Disentangled Multi-granularity Graph Classification Method. In: Campagner, A., Urs Lenz, O., Xia, S., Ślęzak, D., Wąs, J., Yao, J. (eds) Rough Sets. IJCRS 2023. Lecture Notes in Computer Science(), vol 14481. Springer, Cham. https://doi.org/10.1007/978-3-031-50959-9_25

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  • DOI: https://doi.org/10.1007/978-3-031-50959-9_25

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