RAISE: Relational Inference for Label-Efficient Subgraph Classification

In biological and social networks, label-efficient subgraph classification predicts labels for designated subgraphs in a shared background graph from a small labeled training subset. In this setting, predictions depend on which labeled subgraphs are informative for a query. Structural similarity alone does not fully capture source–query relations: structurally similar queries can contain different vertices and relate differently to the same labeled source. These relations may help classify queries with no same-class labeled examples among their closest structural matches. We introduce RAISE (Relation-Aligned Inference from Sparse Examples), which encodes aligned membership and diffusion responses on common background-vertex coordinates in a fixed, label-free relational kernel. Kernel ridge regression learns class-specific source coefficients, while class-wise diffusion aggregation supplies complementary evidence. One round of self-training adds high-confidence pseudo-labeled training subgraphs, followed by a dependence-aware refit using the same kernel. Across four benchmarks at 2–20% label budgets, RAISE improves average micro- and macro-F1 over PADEL by 4.71 and 6.74 percentage points, respectively, and achieves the highest aggregate cross-subset agreement. Controlled studies suggest that these relations add predictive information beyond the evaluated structural representations. Code is available [here](https://anonymous.4open.science/r/iclr2027-RAISE-code-4548).