Affiliations: [a] Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, Xinjiang, China | [b] School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China | [c] Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong, China | [d] Kriston AI Lab, Xiamen, Fujian, China | [e] Data Science and Artificial Intelligence Research Center, School of Computer Science and Engineering, Nanyang Technological University, Singapore
Correspondence:
[*]
Corresponding authors: Pengwei Hu, Kriston AI Lab, Xiamen, Fujian, China. E-mail: [email protected]. Tiantian He, Data Science and Artificial Intelligence Research Center, School of Computer Science and Engineering, Nanyang Technological University, Singapore. E-mail: [email protected].
Abstract: As a fundamental task in cluster analysis, community detection is crucial for the understanding of complex network systems in many disciplines such as biology and sociology. Recently, due to the increase in the richness and variety of attribute information associated with individual nodes, detecting communities in attributed graphs becomes a more challenging problem. Most existing works focus on the similarity between pairwise nodes in terms of both structural and attribute information while ignoring the higher-order patterns involving more than two nodes. In this paper, we explore the possibility of making use of higher-order information in attributed graphs to detect communities. To do so, we first compose tensors to specifically model the higher-order patterns of interest from the aspects of network structures and node attributes, and then propose a novel algorithm to capture these patterns for community detection. Extensive experiments on several real-world datasets with varying sizes and different characteristics of attribute information demonstrated the promising performance of our algorithm.
Keywords: Attributed graph, community detection, clustering, higher-order patterns
