GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck

TL;DR

GaGSL introduces a novel graph structure learning method guided by the Graph Information Bottleneck, which enhances robustness and performance in semi-supervised node classification by learning a compact, informative graph structure.

Contribution

The paper proposes GaGSL, a new approach that combines global feature and structure augmentation with GIB to learn a robust, minimal sufficient graph structure for node classification.

Findings

GaGSL outperforms state-of-the-art methods in accuracy.

GaGSL demonstrates improved robustness to noisy or incomplete graph data.

The method effectively balances performance and robustness through structure redefinition.

Abstract

Graph neural networks (GNNs) are prominent for their effectiveness in processing graph data for semi-supervised node classification tasks. Most works of GNNs assume that the observed structure accurately represents the underlying node relationships. However, the graph structure is inevitably noisy or incomplete in reality, which can degrade the quality of graph representations. Therefore, it is imperative to learn a clean graph structure that balances performance and robustness. In this paper, we propose a novel method named \textit{Global-augmented Graph Structure Learning} (GaGSL), guided by the Graph Information Bottleneck (GIB) principle. The key idea behind GaGSL is to learn a compact and informative graph structure for node classification tasks. Specifically, to mitigate the bias caused by relying solely on the original structure, we first obtain augmented features and augmented structure through global feature augmentation and global structure augmentation. We then input the augmented features and augmented structure into a structure estimator with different parameters for optimization and re-definition of the graph structure, respectively. The redefined structures are combined to form the final graph structure. Finally, we employ GIB based on mutual information to guide the optimization of the graph structure to obtain the minimum sufficient graph structure. Comprehensive evaluations across a range of datasets reveal the outstanding performance and robustness of GaGSL compared with the state-of-the-art methods.