Deep Graph Contrastive Clustering Algorithm Based on Dynamic Threshold Pseudo-label Selection

• Published in: Ji suan ji ke xue Vol. 52; no. 8; pp. 100 - 108
• Main Author: WANG Pei, YANG Xihong, GUAN Renxiang, ZHU En
• Format: Journal Article
• Language: Chinese
• Published: Editorial office of Computer Science 01.08.2025
• Subjects: deep graph clustering|pseudo-label|graph contrastive clustering|graph meural network|dynamic threshold
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.240700112

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