提出因果解耦框架DeCaf，提升图神经网络在分布外数据上的泛化能力
DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification
- 基于结构因果模型重构图数据生成过程，精准定位分布偏移源头
- 独立学习特征与结构的无偏映射，有效缓解多种分布偏移影响
- 在真实与合成数据上验证，适合高安全要求场景的图学习任务
图神经网络（GNN）对分布偏移敏感，在关键领域存在脆弱性和安全隐患。现有方法通常依赖对数据生成过程的过度简化假设，难以反映图数据中分布偏移的真实动态。本文引入结构因果模型（SCMs）构建更现实的图数据生成模型，明确界定分布偏移的来源。基于此，提出因果解耦框架DeCaf，独立学习无偏的特征-标签和结构-标签映射。我们提供了详细的理论分析，证明该方法可有效缓解多种分布偏移的影响。在真实世界和合成数据集上的实验验证了DeCaf在提升GNN泛化性能方面的有效性。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) are susceptible to distribution shifts, creating vulnerability and security issues in critical domains. There is a pressing need to enhance the generalizability of GNNs on out-of-distribution (OOD) test data. Existing methods that target learning an invariant (feature, structure)-label mapping often depend on oversimplified assumptions about the data generation process, which do not adequately reflect the actual dynamics of distribution shifts in graphs. In this paper, we introduce a more realistic graph data generation model using Structural Causal Models (SCMs), allowing us to redefine distribution shifts by pinpointing their origins within the generation process. Building on this, we propose a casual decoupling framework, DeCaf, that independently learns unbiased feature-label and structure-label mappings. We provide a detailed theoretical framework that shows how our approach can effectively mitigate the impact of various distribution shifts. We evaluate DeCaf across both real-world and synthetic datasets that demonstrate different patterns of shifts, confirming its efficacy in enhancing the generalizability of GNNs.
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