Cooperative Explanations of Graph Neural Networks
Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, Tat‐Seng Chua · 2023
With the growing success of graph neural networks (GNNs), the explainability of GNN is attracting considerable attention. Current explainers mostly leverage feature attribution and selection to explain a prediction. By tracing the importance of input features, they select the salient subgraph as the explanation. However, their explainability is at the granularity of input features only, and cannot reveal the usefulness of hidden neurons. This inherent limitation makes the explainers fail to scrutinize the model behavior thoroughly, resulting in unfaithful explanations.