Exploiting higher-order patterns for community detection in attributed graphs

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

27 Scopus Citations
View graph of relations

Author(s)

  • Lun Hu
  • Xiangyu Pan
  • Hong Yan
  • Pengwei Hu
  • Tiantian He

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)207-218
Journal / PublicationIntegrated Computer-Aided Engineering
Volume28
Issue number2
Online published5 Mar 2021
Publication statusPublished - 2021

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.

Research Area(s)

  • Attributed graph, clustering, community detection, higher-order patterns

Citation Format(s)

Exploiting higher-order patterns for community detection in attributed graphs. / Hu, Lun; Pan, Xiangyu; Yan, Hong et al.
In: Integrated Computer-Aided Engineering, Vol. 28, No. 2, 2021, p. 207-218.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review