Towards Multi-Grained Explainability for Graph Neural Networks

When a graph neural network (GNN) made a prediction, one raises question about explainability: "Which fraction of the input graph is most influential to the model's decision?" Producing an answer requires understanding the model's inner workings in general and emphasizing the insights on the decision for the instance at hand. Nonetheless, most of current approaches focus only on one aspect: (1) local explainability, which explains each instance independently, thus hardly exhibits the class-wise patterns; and (2) global explainability, which systematizes the globally important patterns, but might be trivial in the local context. This dichotomy limits the flexibility and effectiveness of explainers greatly. A performant paradigm towards multi-grained explainability is until-now lacking and thus a focus of our work.