Density-based rough set model for hesitant node clustering in overlapping community detection

Jun Wang*, Jiaxu Peng, Ou Liu

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years. A notion of hesitant node (HN) is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure. However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model (DBRSM) is proposed by combining the virtue of density-based algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further 'growth' of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.

Original languageEnglish
Pages (from-to)1089-1097
Number of pages9
JournalJournal of Systems Engineering and Electronics
Volume25
Issue number6
DOIs
Publication statusPublished - 1 Dec 2014

Bibliographical note

This is an open access article freely available on the publisher's website https://ieeexplore.ieee.org/document/7004653

Keywords

  • density-based rough set model (DBRSM)
  • hesitant node (HN)
  • overlapping community detection
  • rough set
  • trust path

Fingerprint Dive into the research topics of 'Density-based rough set model for hesitant node clustering in overlapping community detection'. Together they form a unique fingerprint.

  • Cite this