On Consistency in Graph Neural Network Interpretation

Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions. These identified sub-structures can provide interpretations of GNN's behavior. Though various algorithms are proposed, most of them formalize this task by searching the minimal subgraph which can preserve original predictions. An inductive bias is deep-rooted in this framework: the same output cannot guarantee that two inputs are processed under the same rationale. Consequently, they have the danger of providing spurious explanations and fail to provide consistent explanations. Applying them to explain weakly-performed GNNs would further amplify these issues. To address the issues, we propose to obtain more faithful and consistent explanations of GNNs. After a close examination on predictions of GNNs from the causality perspective, we attribute spurious explanations to two typical reasons: confounding effect of latent variables like distribution shift, and causal factors distinct from the original input. Motivated by the observation that both confounding effects and diverse causal rationales are encoded in internal representations, we propose a simple yet effective countermeasure by aligning embeddings. This new objective can be incorporated into existing GNN explanation algorithms with no effort. We implement both a simplified version based on absolute distance and a distribution-aware version based on anchors. Experiments on $5$ datasets validate its effectiveness, and theoretical analysis shows that it is in effect optimizing a more faithful explanation objective in design, which further justifies the proposed approach.