STAR: A Transferable Explanatory Approach for Graph Neural Networks

Graph neural networks (GNNs) integrate node attributes and relational structure through neighborhood aggregation to capture graph dependencies, but nonlinear computations obscure the decision evidence underlying individual predictions. To improve explainability, post-hoc explanatory methods identify decision evidence without modifying the pretrained GNN's architecture or parameters. Among these approaches, parameterized explanatory methods exploit cross-instance explanatory information to improve explanations for a given pretrained GNN. Extending these explanatory methods to other pretrained GNNs requires accounting for differences in predictive behavior. Although previously acquired explanatory information can guide explanation generation, directly applying the fitted explanation function risks misidentifying the new pretrained GNN's decision evidence. Refitting can require additional samples that are unavailable at explanation time. To address this challenge, this paper proposes STAR, a transferable explanatory approach for GNNs based on Source-informed explanatory prior generation and Target-Adaptive explanation Refinement. To exploit explanatory information across pretrained GNNs, STAR develops a response-conditioned explanatory prior generator that models explanatory relevance in relation to the queried pretrained GNN's behavior and input information. Prediction-aligned optimization across source pretrained GNNs enables explanatory guidance for new pretrained GNNs without generator refitting. To preserve explanation fidelity despite potential source-to-target mismatches, STAR develops target-adaptive explanation refinement that evaluates evidence guided by the explanatory prior alongside locally identified alternatives under the target pretrained GNN. This assessment allows useful explanatory prior guidance to inform the final explanation while permitting correction through target-specific evidence. Experiments across six datasets and three GNN architectures demonstrate competitive explanatory fidelity and effective transfer to target pretrained GNNs excluded from generator fitting.