GEAR: Learning graph neural network explainer via adjusting gradients

Author:

Zhang Youmin,Liu QunORCID,Wang GuoyinORCID,Cheung William K.ORCID,Liu Li

Funder

Chongqing Municipal Education Commission

Natural Science Foundation Project of Chongqing

National Natural Science Foundation of China

Chongqing University of Posts and Telecommunications

Publisher

Elsevier BV

Reference56 articles.

1. A comprehensive survey on graph neural networks;Wu;IEEE Trans. Neural Netw. Learn. Syst.,2021

2. K. Xu, W. Hu, J. Leskovec, S. Jegelka, How Powerful are Graph Neural Networks?, in: International Conference on Learning Representations, 2018, pp. 1–17.

3. MTGCN: A multi-task approach for node classification and link prediction in graph data;Wu;Inf. Process. Manage.,2022

4. Explainability in graph neural networks: A taxonomic survey;Yuan;IEEE Trans. Pattern Anal. Mach. Intell.,2022

5. Z. Ying, D. Bourgeois, J. You, M. Zitnik, J. Leskovec, GNNExplainer: Generating Explanations for Graph Neural Networks, in: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, 2019, pp. 9240–9251.

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