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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. Full article

While collecting training data, even with the manual verification of experts from crowdsourcing platforms, eliminating incorrect annotations (noisy labels) completely is difficult and expensive. In dealing with datasets that contain noisy labels, over-parameterized deep neural networks (DNNs) tend to overfit, leading to poor generalization and classification performance. As a result, noisy label learning (NLL) has received significant attention in recent years. Existing research shows that although DNNs eventually fit all training data, they first prioritize fitting clean samples, then gradually overfit to noisy samples. Mainstream methods utilize this characteristic to divide training data but face two issues: class imbalance in the segmented data subsets and the optimization conflict between unsupervised contrastive representation learning and supervised learning. To address these issues, we propose a Balanced Partitioning and Training framework with Pseudo-Label Relaxed contrastive loss called BPT-PLR, which includes two crucial processes: a balanced partitioning process with a two-dimensional Gaussian mixture model (BP-GMM) and a semi-supervised oversampling training process with a pseudo-label relaxed contrastive loss (SSO-PLR). The former utilizes both semantic feature information and model prediction results to identify noisy labels, introducing a balancing strategy to maintain class balance in the divided subsets as much as possible. The latter adopts the latest pseudo-label relaxed contrastive loss to replace unsupervised contrastive loss, reducing optimization conflicts between semi-supervised and unsupervised contrastive losses to improve performance. We validate the effectiveness of BPT-PLR on four benchmark datasets in the NLL field: CIFAR-10/100, Animal-10N, and Clothing1M. Extensive experiments comparing with state-of-the-art methods demonstrate that BPT-PLR can achieve optimal or near-optimal performance. Full article