Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization

TL;DR

This paper introduces PrunE, a pruning-based method for graph out-of-distribution generalization that removes spurious edges to improve model robustness under distribution shifts.

Contribution

PrunE is the first pruning-based graph OOD method that effectively eliminates spurious edges, enhancing invariant subgraph retention and generalization.

Findings

PrunE outperforms previous methods in OOD tasks.

Pruning spurious edges improves GNN robustness.

Theoretical analysis supports PrunE's effectiveness.

Abstract

Graph Neural Networks (GNNs) often encounter significant performance degradation under distribution shifts between training and test data, hindering their applicability in real-world scenarios. Recent studies have proposed various methods to address the out-of-distribution generalization challenge, with many methods in the graph domain focusing on directly identifying an invariant subgraph that is predictive of the target label. However, we argue that identifying the edges from the invariant subgraph directly is challenging and error-prone, especially when some spurious edges exhibit strong correlations with the targets. In this paper, we propose PrunE, the first pruning-based graph OOD method that eliminates spurious edges to improve OOD generalizability. By pruning spurious edges, PrunE retains the invariant subgraph more comprehensively, which is critical for OOD generalization. Specifically, PrunE employs two regularization terms to prune spurious edges: 1) graph size constraint to exclude uninformative spurious edges, and 2) $\epsilon$-probability alignment to further suppress the occurrence of spurious edges. Through theoretical analysis and extensive experiments, we show that PrunE achieves superior OOD performance and outperforms previous state-of-the-art methods significantly. Codes are available at: \href{https://github.com/tianyao-aka/PrunE-GraphOOD}{https://github.com/tianyao-aka/PrunE-GraphOOD}.