Multi-view Attributed Graph Clustering Based on Contrast Consensus Graph Learning

Abstract

Multi-view attribute graph clustering can divide nodes of graph data with multiple views into different clusters,which has attracted widespread attention from researchers in recent years.At present,many multi-view attribute graph clustering me-thods based on graph neural networks have been proposed and achieved considerable clustering performance.However,since graph neural networks are difficult to deal with graph noise that occurs during data collection,it is difficult for multi-viewattri-bute graph methods based on graph neural networks to further improve clustering performance.Therefore,a new multi-view attribute graph clustering method based on contrastive consensus graph learning is proposed to reduce the impact of noise on clustering and obtain better results.This method consists of four steps.First,graph filtering is used to remove noise on the graph while retaining the intact graph structure.Then,a small number of nodes are selected to learn the consensus graph to reduce computational complexity.Subsequently,graph contrast regularization is used to help learn the consensus graph.Finally,spectral clustering is used to obtain clustering results.A large number of experimental results show that compared with the current state-of-the-art methods,the proposed method can well reduce the impact of noise in graph data on clustering and achieve considerable clustering results with fast execution efficiency.