Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning

Juntao Tan, Shijie Geng, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Yunqi Li, Yongfeng Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Structural data well exists in Web applications, such as social networks in social media, citation networks in academic websites, and threads data in online forums. Due to the complex topology, it is difficult to process and make use of the rich information within such data. Graph Neural Networks (GNNs) have shown great advantages on learning representations for structural data. However, the non-transparency of the deep learning models makes it non-trivial to explain and interpret the predictions made by GNNs. Meanwhile, it is also a big challenge to evaluate the GNN explanations, since in many cases, the ground-truth explanations are unavailable. In this paper, we take insights of Counterfactual and Factual (CF2) reasoning from causal inference theory, to solve both the learning and evaluation problems in explainable GNNs. For generating explanations, we propose a model-agnostic framework by formulating an optimization problem based on both of the two casual perspectives. This distinguishes CF2 from previous explainable GNNs that only consider one of them. Another contribution of the work is the evaluation of GNN explanations. For quantitatively evaluating the generated explanations without the requirement of ground-truth, we design metrics based on Counterfactual and Factual reasoning to evaluate the necessity and sufficiency of the explanations. Experiments show that no matter ground-truth explanations are available or not, CF2 generates better explanations than previous state-of-the-art methods on real-world datasets. Moreover, the statistic analysis justifies the correlation between the performance on ground-truth evaluation and our proposed metrics.

Original languageEnglish (US)
Title of host publicationWWW 2022 - Proceedings of the ACM Web Conference 2022
PublisherAssociation for Computing Machinery, Inc
Number of pages10
ISBN (Electronic)9781450390965
StatePublished - Apr 25 2022
Event31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, France
Duration: Apr 25 2022Apr 29 2022

Publication series

NameWWW 2022 - Proceedings of the ACM Web Conference 2022


Conference31st ACM World Wide Web Conference, WWW 2022
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software


  • Causal Inference
  • Counterfactual Explanation
  • Explainable AI
  • Graph Neural Networks
  • Machine Learning
  • Machine Reasoning


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