WWW 2026 | 南开×北航提出UniGOOD，首次统一图分布外泛化与检测
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TL;DR - UniGOOD is a unified framework for graph out-of-distribution generalization and detection under simultaneous covariate and semantic shifts. It matters because existing graph-learning methods typically address these two real-world distribution shifts separately.
- UniGOOD jointly optimizes a variational invariant-subgraph generator, cross-subgraph spectral contrastive learning, and a three-population invariance regularizer.
- Its contrastive objective clusters positive pairs to support generalization while separating negative pairs to improve semantic-shift detection.
- The framework uses invariant representations for label prediction and KNN distance for parameter-free out-of-distribution detection.
- It achieved the best reported results across the evaluated synthetic and real-world datasets, including a 9.53% relative AUROC gain on GD-Tox21-SIDER and a 9.21% relative FPR reduction on GD-HIV-ZINC.
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WWW 2026 | 南开×北航提出UniGOOD，首次统一图分布外泛化与检测
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TL;DR - UniGOOD is a unified framework for graph out-of-distribution generalization and detection under simultaneous covariate and semantic shifts. It matters because existing graph-learning methods typically address these two real-world distribution shifts separately.
- UniGOOD jointly optimizes a variational invariant-subgraph generator, cross-subgraph spectral contrastive learning, and a three-population invariance regularizer.
- Its contrastive objective clusters positive pairs to support generalization while separating negative pairs to improve semantic-shift detection.
- The framework uses invariant representations for label prediction and KNN distance for parameter-free out-of-distribution detection.
- It achieved the best reported results across the evaluated synthetic and real-world datasets, including a 9.53% relative AUROC gain on GD-Tox21-SIDER and a 9.21% relative FPR reduction on GD-HIV-ZINC.