  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.
