Collaborative Graph Contrastive Network for Semi-Supervised Graph Node Classification

Semi-supervised node classification reduces reliance on labeled data. While GNNs (e.g., GCN, GAT) exploit local structures, they often miss global topology; contrastive methods (e.g., MVGRL) face rigid embeddings or poor view design. We propose the Collaborative Graph Contrastive Network (CGCN) to jointly capture local and global semantics via a dual-encoder framework with cross-view alignment. A fine-grained collaborative constraint preserves node similarity by softly regularizing cross-view correlation matrices, avoiding over-compression. Additionally, a category-aware contrastive loss enhances class compactness and separation with minimal labels. Experiments on six benchmarks show CGCN outperforms state-of-the-art methods. Ablation studies validate each component and demonstrate robustness under complex topologies.