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Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data.
2012
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Ribeiro, M.T., Singh, S., Guestrin, C.: ” why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp. 1135–1144 (2016)
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Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE access 6
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Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM computing surveys (CSUR) 51
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Pasa, F., Golkov, V., Pfeiffer, F., Cremers, D., Pfeiffer, D.: Efficient deep network architectures for fast chest x-ray tuberculosis screening and visualization. Scientific reports 9
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Pope, P.E., Kolouri, S., Rostami, M., Martin, C.E., Hoffmann, H.: Explainability methods for graph convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10772–10781 (2019)
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Chakraborti, T., Sreedharan, S., Kambhampati, S.: The emerging landscape of explainable automated planning & decision making. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20. pp. 4803–4811 (2020)
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Yang, G., Cao, J., Chen, Z., Guo, J., Li, J.: Graph-based neural networks for explainable image privacy inference. Pattern Recognition 105
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Yuan, H., Tang, J., Hu, X., Ji, S.: 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 (2020)
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Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., Cilar, L.: Interpretability of machine learning-based prediction models in healthcare. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10
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Tjoa, E., Guan, C.: A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE Transactions on Neural Networks and Learning Systems (2020)
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Bajaj, M., Chu, L., Xue, Z.Y., Pei, J., Wang, L., Lam, P.C., Zhang, Y.: Robust counterfactual explanations on graph neural networks. In: Ranzato, M., Beygelzimer, A., Dauphin, Y.N., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. pp. 5644–5655 (2021), https://proceedings.neurips.cc/paper/2021/hash/2c8c3a57383c63caef6724343eb62257-Abstract.html
2021
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Yu, S.Y., Malawade, A.V., Muthirayan, D., Khargonekar, P.P., Al Faruque, M.A.: Scene-graph augmented data-driven risk assessment of autonomous vehicle decisions. IEEE Transactions on Intelligent Transportation Systems (2021)
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Yuan, H., Yu, H., Wang, J., Li, K., Ji, S.: On explainability of graph neural networks via subgraph explorations (2021)
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2022
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