Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments

• URL: http://arxiv.org/abs/2511.02354v1
• Date: Tue, 04 Nov 2025 08:22:29 GMT
• Title: Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments
• Authors: Qingyun Sun, Jiayi Luo, Haonan Yuan, Xingcheng Fu, Hao Peng, Jianxin Li, Philip S. Yu,
• Abstract summary: Graph neural networks (GNNs) have shown remarkable success in exploiting the spatial and temporal patterns on dynamic graphs.<n>Existing GNNs exhibit poor ability under distribution shifts, which is inevitable in dynamic scenarios.<n>This paper proposes Evolving Graph Learning framework for evolving graph generalization (Evoal) by environment-aware invariant pattern recognition.
• Score: 61.62036321848316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph neural networks have shown remarkable success in exploiting the spatial and temporal patterns on dynamic graphs. However, existing GNNs exhibit poor generalization ability under distribution shifts, which is inevitable in dynamic scenarios. As dynamic graph generation progresses amid evolving latent non-stationary environments, it is imperative to explore their effects on out-of-distribution (OOD) generalization. This paper proposes a novel Evolving Graph Learning framework for OOD generalization (EvoOOD) by environment-aware invariant pattern recognition. Specifically, we first design an environment sequential variational auto-encoder to model environment evolution and infer the underlying environment distribution. Then, we introduce a mechanism for environment-aware invariant pattern recognition, tailored to address environmental diversification through inferred distributions. Finally, we conduct fine-grained causal interventions on individual nodes using a mixture of instantiated environment samples. This approach helps to distinguish spatio-temporal invariant patterns for OOD prediction, especially in non-stationary environments. Experimental results demonstrate the superiority of EvoGOOD on both real-world and synthetic dynamic datasets under distribution shifts. To the best of our knowledge, it is the first attempt to study the dynamic graph OOD generalization problem from the environment evolution perspective.

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