Toward Interpretable Graph Neural Networks via Concept Matching Model
Author:
Affiliation:
1. Seoul National University,Dept. of Electrical and Computer Engineering,Seoul,South Korea
2. Seoul National University,Graduate School of Data Science,Seoul,South Korea
Funder
National Research Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10415628/10415561/10415803.pdf?arnumber=10415803
Reference26 articles.
1. Deep Learning on Graphs: A Survey
2. Interpretml: A unified framework for machine learning interpretability;Nori;arXiv preprint arXiv:1909.09223,2019
3. Gnnexplainer: Generating explanations for graph neural networks;Ying;Advances in Neural Information Processing Systems,2019
4. Parameterized explainer for graph neural network;Luo;Advances in neural information processing systems,2020
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Toward Interpretable Graph Classification via Concept-Focused Structural Correspondence;Lecture Notes in Computer Science;2024
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