Identifying heterophilic neighbors via confidence-based subgraph matching for graph neural networks

Graph Neural Networks (GNNs) often struggle with heterophilic graphs, where neighboring nodes tend to have dissimilar labels-a common scenario in real-world networks. This paper addresses this limitation through a two-phase framework called ConSM (Confidence-based Subgraph Matching). First, we introduce a confidence-aware subgraph matching module that estimates edge coefficients by comparing the structural similarity of 2-hop neighborhoods using optimal transport. This process identifies task-irrelevant or misleading edges based on a tunable confidence ratio. Second, we integrate these edge coefficients into a sign-aware label propagation mechanism that adaptively encourages or discourages message passing based on edge confidence, thereby enhancing GNN robustness under heterophily. Compared to our earlier conference version [1], this manuscript provides (i) a clearer justification of key design choices such as subgraph-based reasoning and the use of 2-hop neighborhoods, (ii) an adaptive strategy to tune the confidence ratio without manual search, and (iii) extensive new experiments covering recent heterophily-oriented baselines and larger, leakage-free datasets. Empirical results show that ConSM improves classification accuracy, mitigates over-smoothing, and remains effective across both homophilic and heterophilic regimes.