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Buguemar / graphing-a-decision

Public repository of our paper "Graphing a Decision: a Survey for Explainability on Graph-based Learning Models"

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graphing-a-decision

Public repository of our paper "Graphing a Decision: a Survey for Explainability on Graph-based Learning Models"

Scoring-based Explainers

  1. Sundararajan, M., Taly, A., & Yan, Q. (2017, July). Axiomatic attribution for deep networks. In International conference on machine learning (pp. 3319-3328). PMLR. [Paper] [Source Code]
  2. Akita, H., Nakago, K., Komatsu, T., Sugawara, Y., Maeda, S. I., Baba, Y., & Kashima, H. (2018). Bayesgrad: Explaining predictions of graph convolutional networks. In Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part V 25 (pp. 81-92). Springer International Publishing. [Paper] [Source Code]
  3. Baldassarre, F., & Azizpour, H. (2019). Explainability techniques for graph convolutional networks. arXiv preprint arXiv:1905.13686. [Paper] [Source Code]
  4. Pope, P. E., Kolouri, S., Rostami, M., Martin, C. E., & Hoffmann, H. (2019). Explainability methods for graph convolutional neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10772-10781). [Paper]
  5. Schnake, T., Eberle, O., Lederer, J., Nakajima, S., Schütt, K. T., Müller, K. R., & Montavon, G. (2021). Higher-order explanations of graph neural networks via relevant walks. IEEE transactions on pattern analysis and machine intelligence, 44(11), 7581-7596. [Paper] [Source Code]
  6. Chereda, H., Bleckmann, A., Menck, K., Perera-Bel, J., Stegmaier, P., Auer, F., ... & Beißbarth, T. (2021). Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer. Genome medicine, 13, 1-16. [Paper] [Source Code]
  7. Gao, Y., Sun, T., Bhatt, R., Yu, D., Hong, S., & Zhao, L. (2021, December). Gnes: Learning to explain graph neural networks. In 2021 IEEE International Conference on Data Mining (ICDM) (pp. 131-140). IEEE. [Paper] [Source Code]

Extraction-based Explainers

Sequential Paths

  1. Tiddi, I., d’Aquin, M., & Motta, E. (2014). Dedalo: Looking for clusters explanations in a labyrinth of linked data. In The Semantic Web: Trends and Challenges: 11th International Conference, ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014. Proceedings 11 (pp. 333-348). Springer International Publishing. [Paper]
  2. Park, H., Jeon, H., Kim, J., Ahn, B., & Kang, U. (2017). Uniwalk: Explainable and accurate recommendation for rating and network data. arXiv preprint arXiv:1710.07134. [Paper] [Source Code]
  3. Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., & Guo, M. (2018, October). Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM international conference on information and knowledge management (pp. 417-426). [Paper] [Source Code]
  4. Ai, Q., Azizi, V., Chen, X., & Zhang, Y. (2018). Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms, 11(9), 137. [Paper]
  5. Huang, X., Fang, Q., Qian, S., Sang, J., Li, Y., & Xu, C. (2019, October). Explainable interaction-driven user modeling over knowledge graph for sequential recommendation. In proceedings of the 27th ACM international conference on multimedia (pp. 548-556). [Paper]
  6. Qiu, L., Xiao, Y., Qu, Y., Zhou, H., Li, L., Zhang, W., & Yu, Y. (2019, July). Dynamically fused graph network for multi-hop reasoning. In Proceedings of the 57th annual meeting of the association for computational linguistics (pp. 6140-6150). [Paper] [Source Code]
  7. Wang, X., Wang, D., Xu, C., He, X., Cao, Y., & Chua, T. S. (2019, July). Explainable reasoning over knowledge graphs for recommendation. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 5329-5336). [Paper]
  8. Xian, Y., Fu, Z., Muthukrishnan, S., De Melo, G., & Zhang, Y. (2019, July). Reinforcement knowledge graph reasoning for explainable recommendation. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval (pp. 285-294). [Paper] [Source Code]
  9. Xian, Y., Fu, Z., Zhao, H., Ge, Y., Chen, X., Huang, Q., ... & Zhang, Y. (2020, October). CAFE: Coarse-to-fine neural symbolic reasoning for explainable recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 1645-1654). [Paper] [Source Code]
  10. Zhang, Y., Xu, X., Zhou, H., & Zhang, Y. (2020, January). Distilling structured knowledge into embeddings for explainable and accurate recommendation. In Proceedings of the 13th international conference on web search and data mining (pp. 735-743). [Paper]
  11. Bhowmik, R., & de Melo, G. (2020). Explainable link prediction for emerging entities in knowledge graphs. In The Semantic Web–ISWC 2020: 19th International Semantic Web Conference, Athens, Greece, November 2–6, 2020, Proceedings, Part I 19 (pp. 39-55). Springer International Publishing. [Paper] [Source Code]
  12. Zhu, Y., Xian, Y., Fu, Z., De Melo, G., & Zhang, Y. (2021). Faithfully explainable recommendation via neural logic reasoning. arXiv preprint arXiv:2104.07869. [Paper] [Source Code]
  13. Xian, Y., Zhao, H., Lee, T. Y., Kim, S., Rossi, R., Fu, Z., ... & Muthukrishnan, S. (2021, August). Exacta: Explainable column annotation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3775-3785). [Paper]
  14. Song, W., Duan, Z., Yang, Z., Zhu, H., Zhang, M., & Tang, J. (2019). Ekar: an explainable method for knowledge aware recommendation. arXiv preprint arXiv:1906.09506. [Paper]
  15. Zhan, X., Huang, Y., Dong, X., Cao, Q., & Liang, X. (2022). PathReasoner: Explainable reasoning paths for commonsense question answering. Knowledge-Based Systems, 235, 107612. [Paper]
  16. Ma, T., Huang, L., Lu, Q., & Hu, S. (2023). Kr-gcn: Knowledge-aware reasoning with graph convolution network for explainable recommendation. ACM Transactions on Information Systems, 41(1), 1-27. [Paper]

Logic Rules

  1. Guidotti, R., Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., & Giannotti, F. (2018). Local rule-based explanations of black box decision systems. arXiv preprint arXiv:1805.10820. [Paper]
  2. Wang, X., He, X., Feng, F., Nie, L., & Chua, T. S. (2018, April). Tem: Tree-enhanced embedding model for explainable recommendation. In Proceedings of the 2018 world wide web conference (pp. 1543-1552). [Paper] [Source Code]
  3. Ma, W., Zhang, M., Cao, Y., Jin, W., Wang, C., Liu, Y., ... & Ren, X. (2019, May). Jointly learning explainable rules for recommendation with knowledge graph. In The world wide web conference (pp. 1210-1221). [Paper] [Source Code]
  4. Gad-Elrab, M. H., Stepanova, D., Tran, T. K., Adel, H., & Weikum, G. (2020, November). Excut: Explainable embedding-based clustering over knowledge graphs. In International Semantic Web Conference (pp. 218-237). Cham: Springer International Publishing. [Paper] [Source Code]
  5. Du, L., Ding, X., Xiong, K., Liu, T., & Qin, B. (2021, August). Excar: Event graph knowledge enhanced explainable causal reasoning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 2354-2363). [Paper] [Source Code]

Subgraph

  1. Kang, B., Lijffijt, J., & De Bie, T. (2019). Explaine: An approach for explaining network embedding-based link predictions. arXiv preprint arXiv:1904.12694. [Paper]
  2. Ying, Z., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019). Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems, 32. [Paper] [Source Code]
  3. Schlichtkrull, M. S., De Cao, N., & Titov, I. (2020, October). Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking. In International Conference on Learning Representations. [Paper] [Source Code]
  4. Faber, L., Moghaddam, A. K., & Wattenhofer, R. (2020). Contrastive graph neural network explanation. arXiv preprint arXiv:2010.13663. [Paper] [Source Code]
  5. Luo, D., Cheng, W., Xu, D., Yu, W., Zong, B., Chen, H., & Zhang, X. (2020). Parameterized explainer for graph neural network. Advances in neural information processing systems, 33, 19620-19631. [Paper] [Source Code]
  6. Vu, M., & Thai, M. T. (2020). Pgm-explainer: Probabilistic graphical model explanations for graph neural networks. Advances in neural information processing systems, 33, 12225-12235. [Paper] [Source Code]
  7. Wang, X., Wu, Y., Zhang, A., He, X., & Chua, T. S. (2021). Towards multi-grained explainability for graph neural networks. Advances in Neural Information Processing Systems, 34, 18446-18458. [Paper] [Source Code]
  8. Wang, X., Wu, Y., Zhang, A., He, X., & Chua, T. S. (2020). Causal screening to interpret graph neural networks. [Paper]
  9. Lin, W., Lan, H., & Li, B. (2021, July). Generative causal explanations for graph neural networks. In International Conference on Machine Learning (pp. 6666-6679). PMLR. [Paper] [Source Code]
  10. Numeroso, D., & Bacciu, D. (2021, July). Meg: Generating molecular counterfactual explanations for deep graph networks. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. [Paper]
  11. Shan, C., Shen, Y., Zhang, Y., Li, X., & Li, D. (2021). Reinforcement learning enhanced explainer for graph neural networks. Advances in Neural Information Processing Systems, 34, 22523-22533. [Paper]
  12. Dai, E., & Wang, S. (2021, October). Towards self-explainable graph neural network. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 302-311). [Paper] [Source Code]
  13. Deng, S., Rangwala, H., & Ning, Y. (2021, October). Understanding event predictions via contextualized multilevel feature learning. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 342-351). [Paper]
  14. Wu, H., Chen, W., Xu, S., & Xu, B. (2021, June). Counterfactual supporting facts extraction for explainable medical record based diagnosis with graph network. In Proceedings of the 2021 conference of the north American chapter of the association for computational linguistics: human language technologies (pp. 1942-1955). [Paper] [Source Code]
  15. Bajaj, M., Chu, L., Xue, Z. Y., Pei, J., Wang, L., Lam, P. C. H., & Zhang, Y. (2021). Robust counterfactual explanations on graph neural networks. Advances in Neural Information Processing Systems, 34, 5644-5655. [Paper]
  16. Zhang, Y., Defazio, D., & Ramesh, A. (2021, July). Relex: A model-agnostic relational model explainer. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 1042-1049). [Paper]
  17. Yuan, H., Yu, H., Wang, J., Li, K., & Ji, S. (2021, July). On explainability of graph neural networks via subgraph explorations. In International conference on machine learning (pp. 12241-12252). PMLR. [Paper] [Source Code]
  18. Duval, A., & Malliaros, F. D. (2021). Graphsvx: Shapley value explanations for graph neural networks. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part II 21 (pp. 302-318). Springer International Publishing. [Paper] [Source Code]
  19. Zhang, S., Liu, Y., Shah, N., & Sun, Y. (2022). Gstarx: Explaining graph neural networks with structure-aware cooperative games. Advances in Neural Information Processing Systems, 35, 19810-19823. [Paper] [Source Code]
  20. Tan, J., Geng, S., Fu, Z., Ge, Y., Xu, S., Li, Y., & Zhang, Y. (2022, April). Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning. In Proceedings of the ACM Web Conference 2022 (pp. 1018-1027). [Paper]
  21. Funke, T., Khosla, M., Rathee, M., & Anand, A. (2022). Zorro: Valid, sparse, and stable explanations in graph neural networks. IEEE Transactions on Knowledge and Data Engineering. [Paper] [Source Code]
  22. Ji, C., Wang, R., & Wu, H. (2022). Perturb more, trap more: Understanding behaviors of graph neural networks. Neurocomputing, 493, 59-75. [Paper] [Source Code]
  23. Huang, Q., Yamada, M., Tian, Y., Singh, D., & Chang, Y. (2022). Graphlime: Local interpretable model explanations for graph neural networks. IEEE Transactions on Knowledge and Data Engineering. [Paper]
  24. Yu, Z., & Gao, H. (2022). Motifexplainer: a motif-based graph neural network explainer. arXiv preprint arXiv:2202.00519. [Paper]
  25. Xie, Y., Katariya, S., Tang, X., Huang, E., Rao, N., Subbian, K., & Ji, S. (2022). Task-agnostic graph explanations. Advances in Neural Information Processing Systems, 35, 12027-12039. [Paper] [Source Code]
  26. Wang, J., Luo, M., Li, J., Lin, Y., Dong, Y., Dong, J. S., & Zheng, Q. (2023, August). Empower Post-hoc Graph Explanations with Information Bottleneck: A Pre-training and Fine-tuning Perspective. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2349-2360). [Paper]

Generation-based Explainers

  1. Ding, M., Zhou, C., Chen, Q., Yang, H., & Tang, J. (2019, July). Cognitive Graph for Multi-Hop Reading Comprehension at Scale. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 2694-2703). [Paper] [Source Code]
  2. Yuan, H., Tang, J., Hu, X., & Ji, S. (2020, August). Xgnn: Towards model-level explanations of graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 430-438). [Paper]
  3. Saha, S., Yadav, P., Bauer, L., & Bansal, M. (2021, November). ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 7716-7740). [Paper] [Source Code]

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Public repository of our paper "Graphing a Decision: a Survey for Explainability on Graph-based Learning Models"