DCPRES: Contrastive Deep Graph Clustering with Progressive Relaxation Weighting Strategy

Existing contrastive deep graph clustering methods typically employ fixed-threshold strategies when constructing positive and negative sample pairs, and fail to integrate both graph structure information and clustering structure information effectively. However, this fixed-threshold and binary partitioning approach is overly rigid, limiting the model’s utilization of potentially learnable samples. To address this problem, this paper proposes a contrastive deep graph clustering model with a progressive relaxation weighting strategy (DCPRES). By introducing the progressive relaxation weighting strategy (PRES), DCPRES dynamically allocates sample weights, constructing a progressive training strategy from easy to difficult samples. This effectively mitigates the impact of pseudo-label noise and enhances the quality of positive and negative sample pair construction. Building upon this, DCPRES designs two contrastive learning losses: an instance-level loss and a cluster-level loss. These respectively focus on local node information and global cluster distribution characteristics, promoting more robust representation learning and clustering performance. Extensive experiments demonstrated that DCPRES significantly outperforms existing methods on multiple public graph datasets, exhibiting a superior robustness and stability. For instance, on the CORA dataset, our model achieved a significant improvement over the static approach of CCGC, with the NMI increasing by 4.73%, the ACC by 4.77%, the ARI value by 7.03%, and the F1-score by 5.89%. It provides an efficient and stable solution for unsupervised graph clustering tasks.