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October 2, 2025Open Access

Robust OOD Graph Learning via Mean Constraints and Noise Reduction

YZYang ZhouBeijing Institute of Technology XRXiaoning RenJiangsu University

Key Points

The novel approach improves classification accuracy in graph OOD settings by addressing minority class challenges, enhancing robustness.
Constrained Mean Optimization achieved improved reliability for minority classes by aggregating similar instances under worst-case conditions.
The Neighbor-Aware Noise Reweighting method enables dynamic weight adjustments based on local structural consistency, reducing noise impact.
Extensive experiments on both synthetic and real-world datasets validated significant advancements in OOD generalization and classification accuracy.

Abstract

Graph Out-of-Distribution (OOD) classification often suffers from sharp performance drops, particularly under category imbalance and structural noise. This work tackles two pressing challenges in this context: (1) the underperformance of minority classes due to skewed label distributions, and (2) their heightened sensitivity to structural noise in graph data. To address these problems, we propose two complementary solutions. First, Constrained Mean Optimization (CMO) improves minority class robustness by encouraging similarity-based instance aggregation under worst-case conditions. Second, the Neighbor-Aware Noise Reweighting (NNR) mechanism assigns dynamic weights to training samples based on local structural consistency, mitigating noise influence. We provide theoretical justification for our methods, and validate their effectiveness with extensive experiments on both synthetic and real-world datasets, showing significant improvements in Graph OOD generalization and classification accuracy. The code for our method is available at: https://anonymous.4open.science/r/CMO-NNR-2F30.

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