DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks

• URL: http://arxiv.org/abs/2303.02448v1
• Date: Sat, 4 Mar 2023 16:15:25 GMT
• Title: DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks
• Authors: Wenqian Li, Yinchuan Li, Zhigang Li, Jianye Hao, Yan Pang
• Abstract summary: We propose a generative structure -- GFlowNets-based GNN Explainer (GFlowExplainer) Our GFlowExplainer aims to learn a policy that generates a distribution of subgraphs for which the probability of a subgraph is proportional to its' reward. We conduct extensive experiments on both synthetic and real datasets, and both qualitative and quantitative results show the superiority of our GFlowExplainer.
• Score: 30.19635147123557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over the years. Existing literature mainly focus on selecting a subgraph, through combinatorial optimization, to provide faithful explanations. However, the exponential size of candidate subgraphs limits the applicability of state-of-the-art methods to large-scale GNNs. We enhance on this through a different approach: by proposing a generative structure -- GFlowNets-based GNN Explainer (GFlowExplainer), we turn the optimization problem into a step-by-step generative problem. Our GFlowExplainer aims to learn a policy that generates a distribution of subgraphs for which the probability of a subgraph is proportional to its' reward. The proposed approach eliminates the influence of node sequence and thus does not need any pre-training strategies. We also propose a new cut vertex matrix to efficiently explore parent states for GFlowNets structure, thus making our approach applicable in a large-scale setting. We conduct extensive experiments on both synthetic and real datasets, and both qualitative and quantitative results show the superiority of our GFlowExplainer.

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