Dual-Branch Contrastive Learning for Network Representation Learning

Graph Contrastive Learning (GCL) is a self-supervised learning algorithm designed for graph data and has received widespread attention in the field of network representation learning. However, existing GCL-based network representation methods mostly use a single-branch contrastive approach, which makes it difficult to learn deeper semantic relationships and is easily affected by noisy connections during the process of obtaining global structural information embedding. Therefore, this paper proposes a network representation learning method based on a dual-branch contrastive approach. Firstly, the clustering idea is introduced into the process of embedding global structural information, and irrelevant nodes are selected and removed based on the clustering results, effectively reducing the noise in the embedding process. Then, a dual-branch contrastive method, similar to ensemble learning, is proposed, in which the two generated views are compared with the original graph separately, and the joint optimization method is used to continuously update the two views, allowing the model to learn more discriminative feature representations. The proposed method was evaluated on three datasets, Cora, Citeseer, and Pubmed, for node classification and dimensionality reduction visualization experiments. The results show that the proposed method achieved better performance compared to existing baseline models.