DMSD: Validation-Guided Dual-Branch MLP Self-Distillation for Heterophilic Graph Learning

Transferring graph structural knowledge to multilayer perceptrons (MLPs) offers a practical approach to efficient node classification. However, the effectiveness of topological supervision varies across graphs, particularly under heterophily, where local relations and broader propagation contexts provide different learning signals. Our analysis shows that neither route consistently dominates across the evaluated datasets, motivating an alternative to fixed topological supervision. We propose DMSD, a validation-guided, teacher-free dual-branch MLP self-distillation framework for heterophilic graph learning. DMSD independently trains local-relation and propagation-based candidates, each integrating topology-guided and attribute-guided self-distillation to learn complementary structural and feature representations without an external GNN teacher. Rather than uniformly averaging their predictions, DMSD computes a shared pair of fusion weights for each data split by applying a fixed-temperature sigmoid to the candidates’ validation accuracy difference. Their predictions are then combined in log-probability space, without using test labels or training an additional gating network. Experiments on ten node classification datasets show that DMSD achieves the highest mean accuracy among the compared methods on eight datasets, including all five heterophilic graphs, with an average gain of 3.97 percentage points over the strongest baseline on each heterophilic dataset. Ablation results further show that validation-guided soft fusion improves macro-averaged accuracy over uniform fusion by 2.98 percentage points.