Deep attributed graph clustering with feature consistency contrastive and topology enhanced network

Deep attributed graph clustering has attracted considerable interest lately due to its capability to uncover meaningful latent knowledge from heterogeneous spaces, thereby improving our comprehension of real-world systems. However, ensuring the consistency of the clustering assignments generated from topological and attribute information remains a key issue, which is one of the reasons for the low performance of clustering. To tackle these issues, a novel deep clustering approach with Feature Consistency Contrastive and Topology Enhanced Network (FCC-TEN) is proposed, which consists of GAT and AE that can mine the topological and attributed information and achieve consistency contrastive learning to improve clustering performance. First, a Fusion Graph Convolutional Auto-encoder module is proposed to fuse the attribute information captured by each layer of the AE and enrich topological information for improving the feature extraction capability of AE. Then, using a Feature Consistency Contrastive module to uncover consistency information of the GAT and AE through contrastive learning at the feature and label level. Finally, clustering results are obtained directly by the clustering assignment obtained at the label level. Comprehensive testing on five improved datasets shows that our method provides advanced clustering performance. Moreover, visual analyses of the clustering results corroborate a gradual refinement of the clustering structure, proving the validity of our approach.