Adaptive Deep Graph Clustering via Layer-Wise Gated Fusion and Cross-View Contrastive Alignment

Deep graph clustering aims to discover community structures in attributed graphs without labels and is useful for downstream applications such as citation analysis. However, existing methods often cannot make full use of both node features and graph structures, especially when the structure contains noisy links or when attributes and topology are misaligned. Our objective is to learn a robust consensus embedding for attributed graphs under such attribute–topology view misalignment in a fully self-supervised manner. To this end, we propose GDGCA (Gated Deep Graph Clustering with Contrastive Alignment), which combines a dual-stream encoder with layer-wise gated fusion and cross-view contrastive alignment, followed by DEC-style self-training to refine cluster assignments. Experiments on multiple benchmark datasets show that GDGCA achieves competitive clustering performance compared with strong baselines. We further observe stable convergence under noisy or misaligned conditions, indicating improved robustness. Overall, GDGCA provides an effective self-supervised framework for reliable deep graph clustering on real-world attributed graphs.