通过结构外部性提升图神经网络可解释性
Explainable Graph Neural Networks via Structural Externalities
- 用合作博弈论划分节点联盟，将图分解为独立子图
- 引入带外部性的谢林值，量化节点结构贡献度
- 在多种模型上提升解释精度，适合需要可信推理的研究者
图神经网络（GNN）在各类图任务中表现优异，但其“黑箱”特性严重影响可解释性。现有方法难以捕捉节点间复杂的交互模式。本文提出新框架GraphEXT，基于合作博弈论与社会外部性概念，将图节点划分为联盟，将原图分解为独立子图。通过将图结构作为外部性，并结合带外部性的谢林值，量化节点在跨联盟转移时对GNN预测的边际贡献。相比仅关注节点属性的传统谢林值方法，GraphEXT更强调节点间交互及结构变化对预测的影响。在合成与真实数据集上的实验表明，GraphEXT在多种GNN架构下均显著优于基线方法，在保真度方面表现更优，有效提升了GNN模型的可解释性。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have achieved outstanding performance across a wide range of graph-related tasks. However, their "black-box" nature poses significant challenges to their explainability, and existing methods often fail to effectively capture the intricate interaction patterns among nodes within the network. In this work, we propose a novel explainability framework, GraphEXT, which leverages cooperative game theory and the concept of social externalities. GraphEXT partitions graph nodes into coalitions, decomposing the original graph into independent subgraphs. By integrating graph structure as an externality and incorporating the Shapley value under externalities, GraphEXT quantifies node importance through their marginal contributions to GNN predictions as the nodes transition between coalitions. Unlike traditional Shapley value-based methods that primarily focus on node attributes, our GraphEXT places greater emphasis on the interactions among nodes and the impact of structural changes on GNN predictions. Experimental studies on both synthetic and real-world datasets show that GraphEXT outperforms existing baseline methods in terms of fidelity across diverse GNN architectures , significantly enhancing the explainability of GNN models.
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