Abstract: In the real world,there are many complex graph data which includes multiple relations between nodes,namely multi-relation attribute graph.Graph clustering is one of the approaches for mining similar information from graph data.However,most existing graph clustering methods assume that only single type of relation exists between nodes.Even for those that considering the multi-relation of a graph,they use only node attributes for training,or regard graph representation learning and clustering as two completely independent processes.Recently,Deep Graph Infomax(DGI) has shown promising results on many downstream tasks.But there are two major limitations for DGI.Firstly,DGI does not fully explore the various relations among nodes.Secondly,DGI does not jointly optimize the graph representation learning and clustering tasks,resulting in suboptimal clustering results.To address the above-mentioned problems,this paper proposes a novel framework,called clustering method based on contrastive learning for multi-relation attribute graph(CCLMAG),for learning the node embedding suitable for clustering in a unsupervised way.To be more specific,1)The community-level mutual information mechanism is applied to solve the problem of ignoring cluster information by DGI;2)the Embedding Fusion Module is augmented to aggregate the embedding of nodes in different relationships;3)the clustering optimization module is added to link the graph representation learning and clustering so that the learned node representation is more suitable for the clustering task,thus enhancing the interpretability of the clustering results.Extensive experimental results on three multi-relation attribute graph datasets and a real-world futures dataset demonstrate the superiority of CCLMAG compared with the state-of-the-art methods.

Key words: Clustering, Multi-relation attribute graph, Graph contrastive learning, Graph representation learning, Unsupervised learning