DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification
This addresses security and robustness issues for GNNs in critical domains, representing a novel method for a known bottleneck.
The paper tackled the problem of graph neural networks being vulnerable to distribution shifts by proposing DeCaf, a causal decoupling framework that learns unbiased feature-label and structure-label mappings, which enhanced generalizability on out-of-distribution test data across real-world and synthetic datasets.
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.