Structure and Semantic Contrastive Learning for Nodes Clustering in Heterogeneous Information Networks

Nodes clustering is an important approach to partition heterogeneous information networks based on the features and adjacent matrices from different metapaths. Some scholars have adopted contrastive learning methods on the basis of deep clustering, which has achieved promising clustering performance. Despite this, few of them pay attention to redundant information in features, while also not considering both the semantics and structure of the nodes. To fill these gaps, a Structure and Semantic Contrastive Learning for Nodes Clustering in HINs (SSCHC) method is proposed. Specifically, the proposed method explores the high-order neighbor relationship of the node by reconstructing the adjacency matrix containing path and processing the redundant information in the features. In addition, we design a structure and semantic contrastive learning module to obtain more comprehensive information about the nodes. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed SSCHC method compared with the state-of-the-art baselines.