Discover, Ground, Explain: Designing Intrinsically Interpretable GNNs

Molecular graph models can achieve high predictive performance while their attributions poorly localise the substructures underlying the target property. We introduce Graph Variational Motif Prior (GraphVMP), a variational GNN for intrinsic graph explainability that aligns a dedicated latent partition with graphlet descriptors through maximum mean discrepancy (MMD). To learn structural references without concept supervision, we design a novel unsupervised grouping model requiring only upper bounds on subgraph size and the number of distinct motif types as prior information. We also introduce MotifCausalShift, a controlled structural out-of-distribution (OOD) benchmark that varies spurious motif correlations and introduces unseen test-time distractors. On this benchmark, grouping recovers all four planted training motifs and isolates an unseen Star motif without a corresponding learned template. Without annotations specifying which motifs are causal, GraphVMP achieves 97.0 ± 1.0% accuracy and 85.0 ± 6.0% explanation ROC-AUC in the primary MotifCausalShift stress test, improving explanation ROC-AUC by 29.6 percentage points over the baseline without MMD. Following causal-motif replacement, node ROC-AUC remains 85.7 ± 13.7%, compared to 56.0 ± 19.4% without alignment. These results support structural discovery and explicit latent alignment as an approach to designing intrinsic graph explanations whose localisation remains effective under controlled structural shifts.