Reading list on Graph Learning - Explainable artificial intelligence (xAI).

Sat, 19 Aug 2023 00:00:00 +0000

XAI-Graph #

2023 #

[1] Azzolin, S., Longa, A., Barbiero, P., Liò, P., & Passerini, A. (2022). Global explainability of gnns via logic combination of learned concepts. arXiv preprint arXiv:2210.07147.

[2] Miao, S., Luo, Y., Liu, M., & Li, P. (2022). Interpretable Geometric Deep Learning via Learnable Randomness Injection. arXiv preprint arXiv:2210.16966.

[3] Liu, Y., Zhang, X., & Xie, S. (2023, February). A Differential Geometric View and Explainability of GNN on Evolving Graphs. In The Eleventh International Conference on Learning Representations.

[4] Wang, X., & Shen, H. W. (2022). GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks. arXiv preprint arXiv:2209.07924.

[5] Xia, W., Lai, M., Shan, C., Zhang, Y., Dai, X., Li, X., & Li, D. (2023, February). Explaining Temporal Graph Models through an Explorer-Navigator Framework. In The Eleventh International Conference on Learning Representations.

2022 #

[1] Zhang, S., Liu, Y., Shah, N., & Sun, Y. (2022, January). GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games. In Advances in Neural Information Processing Systems.

[2] Xie, Y., Katariya, S., Tang, X., Huang, E., Rao, N., Subbian, K., & Ji, S. (2022). Task-agnostic graph explanations. arXiv preprint arXiv:2202.08335.

[3] Peng, X., Riedl, M., & Ammanabrolu, P. (2022). Inherently explainable reinforcement learning in natural language. Advances in Neural Information Processing Systems, 35, 16178-16190.

[4] Ma, J., Guo, R., Mishra, S., Zhang, A., & Li, J. (2022). CLEAR: Generative Counterfactual Explanations on Graphs. arXiv preprint arXiv:2210.08443.

[5] Xiong, P., Schnake, T., Montavon, G., Müller, K. R., & Nakajima, S. (2022, June). Efficient Computation of Higher-Order Subgraph Attribution via Message Passing. In International Conference on Machine Learning (pp. 24478-24495). PMLR.

[6] Miao, S., Liu, M., & Li, P. (2022, June). Interpretable and generalizable graph learning via stochastic attention mechanism. In International Conference on Machine Learning (pp. 15524-15543). PMLR.

[7] Wu, Y. X., Wang, X., Zhang, A., He, X., & Chua, T. S. (2022). Discovering invariant rationales for graph neural networks. arXiv preprint arXiv:2201.12872.

[8] Feng, Q., Liu, N., Yang, F., Tang, R., Du, M., & Hu, X. (2023). Degree: Decomposition based explanation for graph neural networks. arXiv preprint arXiv:2305.12895.

[9] Tena Cucala, D. J., Cuenca Grau, B., Kostylev, E. V., & Motik, B. (2022). Explainable GNN-based models over knowledge graphs.

[10] Dong, Y., Wang, S., Wang, Y., Derr, T., & Li, J. (2022, August). On structural explanation of bias in graph neural networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 316-326).

[11] Liu, G., Zhao, T., Xu, J., Luo, T., & Jiang, M. (2022, August). Graph rationalization with environment-based augmentations. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 1069-1078).

[12] Wang, P., Cai, R., & Wang, H. (2022, April). Graph-based Extractive Explainer for Recommendations. In Proceedings of the ACM Web Conference 2022 (pp. 2163-2171).

[13] 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).

[14] Islam, S. M., & Bhattacharya, S. (2022, April). AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations. In Proceedings of the ACM Web Conference 2022 (pp. 987-998).

[15] Zhang, Z., Liu, Q., Wang, H., Lu, C., & Lee, C. (2022, June). Protgnn: Towards self-explaining graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 8, pp. 9127-9135).

[16] Feng, A., You, C., Wang, S., & Tassiulas, L. (2022, June). Kergnns: Interpretable graph neural networks with graph kernels. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 6, pp. 6614-6622).

[17] Aglionby, G., & Teufel, S. (2022, December). Faithful Knowledge Graph Explanations in Commonsense Question Answering. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 10811-10817).

[18] Li, X., Zhang, X., JiaHao, P., Mao, R., Zhou, M., Xie, X., & Liao, H. (2022, December). A Joint Learning Framework for Restaurant Survival Prediction and Explanation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 3285-3297).

2021 #

[1] 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.

[2] 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.

[3] 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.

[4] 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.

[5] 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.

[6] Henderson, R., Clevert, D. A., & Montanari, F. (2021, July). Improving molecular graph neural network explainability with orthonormalization and induced sparsity. In International Conference on Machine Learning (pp. 4203-4213). PMLR.

[7] Wang, X., Fan, S., Kuang, K., & Zhu, W. (2021, July). Explainable automated graph representation learning with hyperparameter importance. In International Conference on Machine Learning (pp. 10727-10737). PMLR.

[8] Faber, L., K. Moghaddam, A., & Wattenhofer, R. (2021, August). When comparing to ground truth is wrong: On evaluating gnn explanation methods. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 332-341).

[9] Abrate, C., & Bonchi, F. (2021, August). Counterfactual graphs for explainable classification of brain networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 2495-2504).

[10] Liu, Y., Chen, C., Liu, Y., Zhang, X., & Xie, S. (2021, December). Multi-objective Explanations of GNN Predictions. In 2021 IEEE International Conference on Data Mining (ICDM) (pp. 409-418). IEEE.

[11] 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.

[12] Fan, Y., Yao, Y., & Joe-Wong, C. (2021, December). Gcn-se: Attention as explainability for node classification in dynamic graphs. In 2021 IEEE International Conference on Data Mining (ICDM) (pp. 1060-1065). IEEE.

2020 #

[1] 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.

[2] 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.