Triple-view graph clustering network based on high-confidence contrastive learning strategy

Abstract

Recent contrastive deep clustering models have seen considerable success. However, many of these approaches often focus on distinguishing between nodes in two views for contrastive learning, which can pose significant difficulties when handling complex noisy nodes. Furthermore, numerous deep clustering models do not have a dependable framework for choosing positive and negative sample pairs. To tackle these challenges, we introduce the Triple-View Graph Clustering Network with a High-Confidence Contrastive Learning Strategy (TGCN-HCC). This model comprises two primary components. The first is a Triple-View fusion network that features parameter-shared Siamese encoders and a graph attention network, which produces semantically rich fused embeddings by combining embeddings from the three views. The second component is a self-supervised clustering module that utilizes high-confidence pseudo label screening. This module incorporates a loss function that uses high-confidence pseudo label to enhance the clustering process. Comprehensive experiments on five datasets indicate that our proposed model surpasses other clustering models in performance.