论文丨J. Chen, H. Mao, W. L. Woo, C. Liu*, and X. Peng：Cross-view graph consistency learning for invariant graph representations

本文（Cross-view graph consistency learning for invariant graph representations）原载Cross-view graph consistency learning for invariant graph representations，由四川大学陈杰副教授等科研人员创作，系四川大学智慧法治超前部署学科系列学术成果。后续会持续分享四川大学智慧法治超前部署学科系列学术成果，欢迎大家阅读。

Abstract:Graph representation learning is fundamental for analyzing graph-structured data. Exploring invariant graph representations remains a challenge for most existing graph representation learning methods. In this paper, we propose a cross-view graph consistency learning (CGCL) method that learns invariant graph representations for link prediction. First, two complementary augmented views are derived from an incomplete graph structure through a coupled graph structure augmentation scheme. This augmentation scheme mitigates the potential information loss that is commonly associated with various data augmentation techniques involving raw graph data, such as edge perturbation, node removal, and attribute masking. Second, we propose a CGCL model that can learn invariant graph representations. A cross-view training scheme is proposed to train the proposed CGCL model. This scheme attempts to maximize the consistency information between one augmented view and the graph structure reconstructed from the other augmented view. Furthermore, we offer a comprehensive theoretical CGCL analysis. This paper empirically and experimentally demonstrates the effectiveness of the proposed CGCL method, achieving competitive results on graph datasets in comparisons with several state-of-the-art algorithms.

J. Chen, H. Mao, W. L. Woo, C. Liu*, and X. Peng, “Cross-view graph consistency learning for invariant graph representations”, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 15, 15795-15802, Apr. 2025.（论文下载）