In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines. Unlike the most existing explanation methods, MSInterpreter provides a Message-passing Selection scheme(MSScheme) to select the critical paths for GNNs' message aggregations, which aims at reaching the self-explaination instead of post-hoc explanations. In detail, the elaborate MSScheme is designed to calculate weight factors of message aggregation paths by considering the vanilla structure and node embedding components, where the structure base aims at weight factors among node-induced substructures; on the other hand, the node embedding base focuses on weight factors via node embeddings obtained by one-layer GNN.Finally, we demonstrate the effectiveness of our approach on graph classification benchmarks.

翻译：本文致力于开发一种可解释的图神经网络推理范式，称为MSInterpreter，该范式可作为即插即用方案，便捷地应用于各类图神经网络基线模型。与现有大多数解释方法不同，MSInterpreter提出了一种消息传递选择方案（MSScheme），用于选择图神经网络消息聚合的关键路径，旨在实现自解释而非事后解释。具体而言，该精细设计的MSScheme通过考虑原始结构与节点嵌入组件来计算消息聚合路径的权重因子：其中结构基组件旨在计算节点诱导子结构之间的权重因子；而节点嵌入基组件则通过单层图神经网络获得的节点嵌入来聚焦于权重因子的计算。最后，我们在图分类基准上验证了该方法的有效性。