Abstract: In recent years,graph neural networks have performed well in processing complex structural data,and are widely used in node classification,graph classification,link prediction and other fields.Deep graph clustering combines the powerful representation ability of GNNs with the goal of clustering algorithms to discover hidden population structures from complex graph structure data.However,the existing pseudo-label-based graph clustering algorithms often use fixed thresholds to filter samples according to categories to obtain high-confidence sample data to guide model optimization.However,the method of fixed thresholds can lead to category imbalance,which in turn affects the performance of model clustering.In order to solve the above problems,this paper proposes a contrastive clustering algorithm based on dynamic threshold pseudo-label depth map.Specifically,two multilayer perceptron(MLP) structures that do not share parameters are used to capture the latent structural features of the graph data,and the K-Means algorithm is used to obtain the clustering results.On this basis,the trust strength is introduced to dynamically adjust the threshold for obtaining pseudo-labels,and the number of high-confidence samples in each category is dynamically adjusted during the training process to alleviate the problem of category imbalance.In addition,this paper optimizes the contrastive learning strategy,improves the construction method of sample pairs,and improves the discriminant ability of the model.Experimental results show that the proposed method performs well on the six benchmark datasets,surpassing the existing methods in multiple evaluation indicators,and strongly demonstrates the effectiveness of the proposed algorithm.

Key words: Deep graph clustering, Pseudo-label, Graph contrastive clustering, Graph meural network, Dynamic threshold