[Paper Review] Learning Invariant Representations of Graph Neural Networks via Cluster Generalization

Donglin Xia, Xiao Wang+2 more|arXiv (Cornell University)|Mar 6, 2024

Advanced Graph Neural Networks4 citations

TL;DR

This paper proposes a plug-in mechanism, Cluster Information Transfer (CIT), that enhances graph neural networks' robustness to graph structure shift by transferring cluster-level statistics (mean and variance) across nodes while preserving cluster-independent features. The method improves generalization across diverse and unknown test graphs, with empirical results showing consistent performance gains across multiple structure shift scenarios.

ABSTRACT

Graph neural networks (GNNs) have become increasingly popular in modeling graph-structured data due to their ability to learn node representations by aggregating local structure information. However, it is widely acknowledged that the test graph structure may differ from the training graph structure, resulting in a structure shift. In this paper, we experimentally find that the performance of GNNs drops significantly when the structure shift happens, suggesting that the learned models may be biased towards specific structure patterns. To address this challenge, we propose the Cluster Information Transfer (CIT) mechanism (Code available at https://github.com/BUPT-GAMMA/CITGNN), which can learn invariant representations for GNNs, thereby improving their generalization ability to various and unknown test graphs with structure shift. The CIT mechanism achieves this by combining different cluster information with the nodes while preserving their cluster-independent information. By generating nodes across different clusters, the mechanism significantly enhances the diversity of the nodes and helps GNNs learn the invariant representations. We provide a theoretical analysis of the CIT mechanism, showing that the impact of changing clusters during structure shift can be mitigated after transfer. Additionally, the proposed mechanism is a plug-in that can be easily used to improve existing GNNs. We comprehensively evaluate our proposed method on three typical structure shift scenarios, demonstrating its effectiveness in enhancing GNNs' performance.

Motivation & Objective

• To address the performance degradation of GNNs under graph structure shift, a common form of distribution shift in real-world graphs.
• To develop a method that enables GNNs to learn invariant representations robust to changes in graph topology during inference.
• To provide a plug-in solution compatible with existing GNN architectures that enhances generalization without architectural overhaul.
• To theoretically and empirically validate that cluster information transfer mitigates the impact of changing cluster assignments during structure shift.

Proposed method

• Apply GNNs to learn initial node representations, then use spectral clustering to identify cluster structures in the graph.
• Characterize each cluster using two statistics: cluster mean and cluster variance, which serve as transferable information.
• Transfer nodes to new clusters by reassigning them based on these statistics while preserving their intrinsic, cluster-independent features.
• Train GNNs on the newly generated node representations to improve generalization across different cluster configurations.
• Integrate the CIT mechanism as a plug-in layer that can be applied to various GNN models with minimal modification.
• Theoretical analysis shows that the impact of cluster shifts during inference is mitigated after transfer, supporting robustness.

Experimental results

Research questions

• RQ1 Can GNNs maintain stable performance when the test graph structure differs from the training structure?
• RQ2 How can invariant representations be learned in GNNs to improve robustness under structure shift?
• RQ3 Can cluster-level statistics be effectively used to generate diverse training environments for improved generalization?
• RQ4 To what extent does the CIT mechanism reduce performance degradation due to graph structure shift?

Key findings

• The performance of standard GNNs degrades significantly under structure shift, indicating strong bias toward specific graph structures.
• The CIT mechanism consistently improves GNN performance across three typical structure shift scenarios, including edge addition and removal.
• The model's accuracy correlates strongly with clustering quality, as measured by the silhouette coefficient, indicating that effective clustering enhances CIT performance.
• The method is robust to hyperparameters such as the number of clusters and transfer probability, showing stable performance across different configurations.
• Theoretical analysis confirms that the impact of cluster changes during structure shift is mitigated after information transfer via CIT.
• The CIT mechanism is a plug-and-play enhancement that can be applied to various GNN models without architectural changes.