Topological and semantic contrastive graph clustering by Ricci curvature augmentation and hypergraph fusion

Contrastive graph clustering is an advanced technology in the field of cluster analysis. By leveraging graph neural networks and contrastive learning paradigm, it enables the coupling of topological structure and node semantic information for attributed graph networks. Graph augmentation and positive sample selection are two essentials of contrastive graph clustering. However, existing graph augmentation methods tend to disrupt the cluster structures, and most positive sample selectors suffer from the false negative sample problem. In this paper, we propose a Topological and Semantic Contrastive Graph Clustering (TSCGC) model consisting of three learning components. The representation learning component augments original graph using Ricci curvature to preserve the cluster structure, and introduces hypergraph view to capture high-order relationships. Graph and hypergraph convolutional networks are used to encode the triple-view embeddings. Meanwhile, we develop a dual contrastive learning component to extract the topological and semantic information. To reduce the number of false negatives, it utilizes K-means to generate pseudo cluster labels to guide the selection of positive samples. The self-supervised learning component is leveraged to align the three graph views. The final clustering results are obtained by performing K-means on the aligned embeddings. We demonstrated the effectiveness by comparing the performance of TSCGC with 13 clustering baselines on six real-world networks. Ablations verified the validity of key components and the impact of parameter settings were also analyzed. We further applied TSCGC to identify the function types of 10,370 buildings in ShenZhen City, China based on multi-source geospatial data. It achieved the highest accuracy and exhibit significant potential in handling complex network structures and high-dimensional node features. The code is available at: https://github.com/ZPGuiGroupWhu/TSCGC .