Dynamic graph attention-guided graph clustering with entropy minimization self-supervision

Graph clustering is one of the most fundamental tasks in graph learning. Recently, numerous graph clustering models based on dual network (Auto-encoder+Graph Neural Network(GNN)) architectures have emerged and achieved promising results. However, we observe several limitations in the literature: 1) simple graph neural networks that fail to capture the intricate relationships between nodes are used for graph clustering tasks; 2) heterogeneous information is inadequately interacted and merged; and 3) the clustering boundaries are fuzzy in the feature space. To address the aforementioned issues, we propose a novel graph clustering model named Dynamic Graph Attention-guided Graph Clustering with Entropy Minimization self-supervision(DGAGC-EM). Specifically, we introduce DGATE, a graph auto-encoder based on dynamic graph attention, to capture the intricate relationships among graph nodes. Additionally, we perform feature enhancement from both global and local perspectives via the proposed Global-Local Feature Enhancement (GLFE) module. Finally, we propose a self-supervised strategy based on entropy minimization theory to guide network training process to achieve better performance and produce sharper clustering boundaries. Extensive experimental results obtained on four datasets demonstrate that our method is highly competitive with the SOTA methods.

The figure presents the overall framework of proposed Dynamic Graph Attention-guided Graph Clustering with Entropy Minimization selfsupervision(DGAGC-EM). Specifically, the Dynamic Graph Attetion Auto-Encoder Module is our proposed graph auto-encoder based on dynamic graph attention, to capture the intricate relationships among graph nodes. The Auto-Encoder Module is a basic autoencoder with simple MLPs to extract embeddings from node attributes. Additionally, the proposed Global-Local Feature Enhancement (GLFE) module perform feature enhancement from both global and local perspectives. Finally, the proposed Self-supervised Module guide network training process to achieve better performance and produce sharper clustering boundaries