A one-step graph clustering method on heterogeneous graphs via variational graph embedding

Abstract

Graph clustering is one of the fundamental tasks in graph data mining, with significant applications in social networks and recommendation systems. Traditional methods for clustering heterogeneous graphs typically involve obtaining node representations as a preliminary step, followed by the application of clustering algorithms to achieve the final clustering results. However, this two-step approach leads to a disconnection between the optimization of node representation and the clustering process, making it challenging to achieve optimal results. In this paper, we propose a graph clustering approach specifically designed for heterogeneous graphs that unifies the optimization of node representation and the clustering process for nodes in a heterogeneous graph. We assume that the relationships between different meta-paths in the heterogeneous graph are mutually independent. By maximizing the joint probability of meta-paths and nodes, we derive the optimization objective through variational methods. Finally, we employ backpropagation and reparameterization techniques to optimize this objective and thereby achieve the desired clustering results. Experiments conducted on multiple real heterogeneous datasets demonstrate that the proposed method is competitive with existing methods.

Keywords

• heterogeneous graph
• graph cluster
• variational graph autoencoder
• graph neural network