Attribute Graph Clustering Based on Self-Supervised Spectral Embedding Network

Abstract

Attribute graph clustering requires joint modeling of both graph structure and node properties, which is challenging. In recent years, graph neural networks have been utilized to mine deep information on attribute graphs through feature aggregation, learning node embeddings, and using traditional methods to obtain clustering results, exhibiting excellent clustering performance. However, these approaches often face the following issues: the original graph structure and node features contain noise, and the quality dramatically affects the clustering results; the two-step framework of first learning node embeddings and then clustering is often suboptimal as it is not target-oriented and prone to producing suboptimal results. Through research, we propose an attribute graph clustering method called FK-SENet based on a self-supervised spectral embedding network. It utilizes Laplacian smoothing filters to smooth and denoise node features. It optimizes the initial graph structure by leveraging shared neighbor information to improve the quality of the original data, thereby enhancing clustering performance. Soft labels are generated from the node embeddings themselves to achieve self-supervision, and they jointly guide the clustering process with spectral clustering loss, iteratively optimizing the clustering results. The effectiveness of this model has been demonstrated through extensive experiments and comparisons with baseline methods.

Keywords

• Attribute graph clustering
• self-supervised learning
• spectral embedding network
• laplacian smoothing