An approach for hesitant node classification in overlapping community detection

Jun Wang, Jiaxu Peng, Ou Liu

Research output: Contribution to conferencePaper

Abstract

Overlapping community detection has recently drawn much attention in the field of social network analysis. In this paper, we propose a notion of hesitant node (HN) in network with overlapping community structure. An HN is a special kind of node that contacts with multiple communities but the communication is not frequent or even accidental, thus its community structure is implicit and its classification is ambiguous. Besides, HNs are not rare to be found in networks and may even take up a large number of the nodes in the network, just like the long tail. They should either be classified into certain communities which would promote their development in the network or regarded as the hubs if they are the efficient junctions between different communities. Current approaches have difficulties in identifying and processing HNs. In this paper, a quantitative method based on the Density-Based Rough Set Model (DBRSM) is proposed by combining the advantages of density-based algorithms and rough set model. Our experiments on the real-world and synthetic datasets show the advancement of our approach. HNs are classified into communities which are more similar with them and the classification process enhances the modularity as well.

Original languageEnglish
Publication statusPublished - 1 Jan 2014
Event18th Pacific Asia Conference on Information Systems, PACIS 2014 - Chengdu, China
Duration: 24 Jun 201428 Jun 2014

Conference

Conference18th Pacific Asia Conference on Information Systems, PACIS 2014
CountryChina
CityChengdu
Period24/06/1428/06/14

Keywords

  • Community detection
  • Density
  • Hesitant node
  • Rough set
  • Trust path

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