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Finding attribute diversified community over large attributed networks

Abstract

Well connected users are generally discovered in communities which is one of the most important tasks for network data analytics and has tremendous real applications. In recent years, community search in attributed graphs has begun to attract attention, which aims to find communities that are both structure and attribute cohesive. Meanwhile, searching a community that is structure cohesive but attribute diversified, denoted as attribute diversified community search, is still at an early stage. In this paper, we introduce our recent effort for discovering attribute diversified community. In fact, for different applications, the needs of attribute diversification for modelling the community are quite different. We introduce three attribute diversified community models in which attribute diversification takes different roles for presenting as an objective and as a constraint. We also discuss major techniques for speeding up the attribute diversified community search. We conduct extensive experiments to show the effectiveness and efficiency of our algorithms for finding attribute diversified communities in various settings.

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Acknowledgements

The work was supported by Australia Research Council discovery projects DP170104747 and DP200103700.

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Correspondence to Chengfei Liu.

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This article belongs to the Topical Collection: Special Issue on Large Scale Graph Data Analytics

Guest Editors: Xuemin Lin, Lu Qin, Wenjie Zhang, and Ying Zhang

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Chowdhary, A.A., Liu, C., Chen, L. et al. Finding attribute diversified community over large attributed networks. World Wide Web (2021). https://doi.org/10.1007/s11280-021-00891-6

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Keywords

  • Community search
  • Diversity
  • User engagement
  • Attributed networks
  • Indexing