Vertex Entanglement-Enhanced Graph Contrastive Learning for Social Networks

Graph contrastive learning can extract high-quality representations from graph-structured data, providing an effective way to analyze social networks. However, existing graph contrastive learning methods with both random augmentations and advanced adaptive strategies struggle to effectively preserve key structural semantics and eliminate graph noise, which ultimately limits representation quality. To this end, this article proposes a vertex entanglement-enhanced graph contrastive learning (VEGCL), which utilizes vertex entanglement (VE) as a novel metric that quantifies node importance from a global structural perspective to guide the removal of unimportant nodes and edges for generating augmented views. Specifically, VE is initially employed to selectively remove less important nodes, generating the first augmented view. Subsequently, the original graph is transformed into a line graph, after which VE is again applied to remove less critical edges, producing the second augmented view. Following augmentation, model training is guided by the Info Noise Contrastive Estimation loss function to enhance embedding quality. To verify the effectiveness of the proposed approach, node classification experiments are conducted on five commonly used benchmark datasets including Cora, PubMed, CiteSeer, Coauthor-CS, and LastFM Asia. The experimental results demonstrate that the proposed method improves the performance of node classification by 0.5% ∼ 2.3% , and 0.04% ∼ 5% , in terms of accuracy and F1 score compared with the existing baseline methods.