FlowX: Towards Explainable Graph Neural Networks via Message Flows

• URL: http://arxiv.org/abs/2206.12987v3
• Date: Fri, 29 Dec 2023 21:28:49 GMT
• Title: FlowX: Towards Explainable Graph Neural Networks via Message Flows
• Authors: Shurui Gui, Hao Yuan, Jie Wang, Qicheng Lao, Kang Li, Shuiwang Ji
• Abstract summary: We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms. We propose a novel method here, known as FlowX, to explain GNNs by identifying important message flows. We then propose an information-controlled learning algorithm to train flow scores toward diverse explanation targets.
• Score: 59.025023020402365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms. While most current methods focus on explaining graph nodes, edges, or features, we argue that, as the inherent functional mechanism of GNNs, message flows are more natural for performing explainability. To this end, we propose a novel method here, known as FlowX, to explain GNNs by identifying important message flows. To quantify the importance of flows, we propose to follow the philosophy of Shapley values from cooperative game theory. To tackle the complexity of computing all coalitions' marginal contributions, we propose a flow sampling scheme to compute Shapley value approximations as initial assessments of further training. We then propose an information-controlled learning algorithm to train flow scores toward diverse explanation targets: necessary or sufficient explanations. Experimental studies on both synthetic and real-world datasets demonstrate that our proposed FlowX and its variants lead to improved explainability of GNNs. The code is available at https://github.com/divelab/DIG.

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