Cross-View Graph Consistency Learning for Invariant Graph Representations

2023-11-20

arXiv.org

AI Summary

Link prediction on incomplete graphs suffers from difficulty in modeling graph representation invariance under structural perturbations. Method: This paper proposes a coupled graph augmentation and cross-view consistency learning framework. It generates two structurally complementary graph views via tailored augmentation strategies, jointly optimizes graph autoencoder reconstruction and cross-view contrastive loss, and incorporates a consistency regularization constraint to enforce robustness against structural perturbations. Contribution/Results: Theoretical analysis establishes convergence guarantees and formal invariance properties of the learned representations. Extensive experiments on multiple benchmark datasets demonstrate significant improvements over state-of-the-art methods in link prediction performance, validating the strong generalization capability and practical effectiveness of the proposed approach.

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.

Problem

• Learning invariant graph representations
• Cross-view consistency for link prediction
• Mitigating information loss in graph augmentation

Innovation

• Cross-view graph consistency learning
• Coupled graph structure augmentation
• Maximize consistency between augmented views