Deep Contrastive Clustering via Hard positive sample Debiased

Abstract

Deep graph clustering aims to reveal the underlying information of the graph and provide accurate embedding for the node clustering task, in which contrastive learning plays an important role. However, the commonly used contrastive loss function incorrectly classifies elements outside the diagonal of the cross view as negative samples, which contain a large number of positive sample pairs. In order to overcome the above problems, we propose a new deep graph contrastive clustering method, which combines hard positive sample debiasing and sample pair weighting, and improves the recognition ability of the network by removing the potential positive sample pairs and hard sample pair weighting in the negative sample pair of the loss function. More specifically, we developed a symmetric graph neural network to encode node representations. Using two sets of node representations, the correctness of negative cases is increased by clustering to generate high-confidence pseudo-label pairs for labels and confidence. The similarity distribution differences are weighted by adapting to different dataset samples to improve the sample recognition ability. To verify the efficacy of our DCHD, we compare it to existing state-of-the-art methods for node clustering tasks on six real-world datasets. Overall, the experimental results show that our proposed method outperforms current state-of-the-art graph clustering methods.