IENE：识别与外推节点环境以实现图上的分布外泛化
IENE: Identifying and Extrapolating the Node Environment for Out-of-Distribution Generalization on Graphs
AI总结：
针对图神经网络在分布偏移下因特征与拓扑混淆因素导致性能下降的问题，提出IENE方法，利用解耦信息瓶颈进行节点级环境识别与不变特征学习，并通过图增强外推拓扑环境识别结构不变性，有效提升了图分布外泛化能力。
摘要：
由于图神经网络（GNNs）在分布偏移下的性能退化，图上的分布外（OOD）泛化工作受到了广泛关注。一种新颖的视角涉及通过环境识别来区分来自不同环境的潜在混淆偏差，使模型能够摆脱对环境敏感的相关性，并在分布偏移下保持稳定的性能。然而，在图数据中，混淆因素不仅影响节点特征的生成过程，还影响节点之间的复杂交互。我们观察到，忽略它们中的任何一个方面都会导致性能下降。在本文中，我们提出了IENE，一种基于节点级环境识别与外推技术的图OOD泛化方法。它同时从两个粒度强化了模型提取不变性的能力，从而提升了泛化性。具体而言，为了识别特征中的不变性，我们利用解耦信息瓶颈框架来实现节点级环境估计与不变特征学习之间的相互促进。此外，我们通过图增强技术外推拓扑环境以识别结构不变性。我们用具体的算法实现了这一概念性方法，并为我们的方法提供了理论分析和证明。在两个合成和四个真实世界OOD数据集上的广泛实验评估验证了IENE的优越性，它优于现有技术，并为增强GNNs的泛化能力提供了灵活的框架。
英文摘要：
Due to the performance degradation of graph neural networks (GNNs) under distribution shifts, the work on out-of-distribution (OOD) generalization on graphs has received widespread attention. A novel perspective involves distinguishing potential confounding biases from different environments through environmental identification, enabling the model to escape environmentally-sensitive correlations and maintain stable performance under distribution shifts. However, in graph data, confounding factors not only affect the generation process of node features but also influence the complex interaction between nodes. We observe that neglecting either aspect of them will lead to a decrease in performance. In this paper, we propose IENE, an OOD generalization method on graphs based on node-level environmental identification and extrapolation techniques. It strengthens the model's ability to extract invariance from two granularities simultaneously, leading to improved generalization. Specifically, to identify invariance in features, we utilize the disentangled information bottleneck framework to achieve mutual promotion between node-level environmental estimation and invariant feature learning. Furthermore, we extrapolate topological environments through graph augmentation techniques to identify structural invariance. We implement the conceptual method with specific algorithms and provide theoretical analysis and proofs for our approach. Extensive experimental evaluations on two synthetic and four real-world OOD datasets validate the superiority of IENE, which outperforms existing techniques and provides a flexible framework for enhancing the generalization of GNNs.
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