演化图学习用于非平稳环境中的分布外泛化
Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments
AI总结：
本文提出EvoOOD框架，通过环境感知的不变模式识别提升动态图在非平稳环境中的分布外泛化能力。
摘要：
图神经网络在动态图上利用空间和时间模式方面表现出显著的成功。然而，现有GNN在分布偏移下泛化能力差，这在动态场景中是不可避免的。随着动态图生成在演变的潜在非平稳环境中进行，有必要探索其对分布外（OOD）泛化的影响。本文提出了一种新的演化图学习框架用于OOD泛化（EvoOOD），通过环境感知的不变模式识别。具体来说，我们首先设计了一个环境序列变分自动编码器来建模环境演变并推断潜在环境分布。然后，我们引入了一种环境感知的不变模式识别机制，专门针对通过推断分布解决环境多样性。最后，我们使用实例化环境样本的混合物对单个节点进行细粒度因果干预。这种方法有助于区分时空不变模式以进行OOD预测，特别是在非平稳环境中。实验结果表明，EvoOOD在分布偏移下的真实世界和合成动态数据集上均表现出优越性。据我们所知，这是首次从环境演变的角度研究动态图OOD泛化问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
