DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification

• URL: http://arxiv.org/abs/2410.20295v1
• Date: Sun, 27 Oct 2024 00:22:18 GMT
• Title: DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification
• Authors: Xiaoxue Han, Huzefa Rangwala, Yue Ning,
• Abstract summary: Graph Neural Networks (GNNs) are susceptible to distribution shifts, creating vulnerability and security issues in critical domains. Existing methods that target learning an invariant (feature, structure)-label mapping often depend on oversimplified assumptions about the data generation process. We introduce a more realistic graph data generation model using Structural Causal Models (SCMs) We propose a casual decoupling framework, DeCaf, that independently learns unbiased feature-label and structure-label mappings.
• Score: 14.96980804513399
• License:
• Abstract: 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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