arXiv Machine Learning October 7, 2026 By Qingying Hao, Zikang Chen, Chuxuan Hu, Jinyuan Jia, Bo Li, Gang Wang, Carl Gunter

Complementary Supervised and Self-Supervised Representations for Out-of-Distribution Graph Learning

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The paper investigates how self‑supervised graph representations can complement supervised ones for out‑of‑distribution (OOD) node classification. Two backbone‑agnostic frameworks are introduced: Co‑Train, which jointly learns and integrates supervised and SSL representations during training, and Dual‑Space Retrieval, which performs non‑parametric prediction in both representation spaces and fuses the results at inference. Experiments on four graph benchmarks with various GNN backbones and SSL objectives (DGI and GRACE) show that Co‑Train consistently outperforms strong supervised OOD baselines, while Dual‑Space Retrieval offers competitive, flexible performance, demonstrating that SSL provides complementary signals that improve OOD node classification.

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