Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21019
Title: Density-based rough set model for hesitant node clustering in overlapping community detection
Authors: Wang, J
Peng, J
Liu, O 
Keywords: Density-based rough set model (DBRSM)
Hesitant node (HN)
Overlapping community detection
Rough set
Trust path
Issue Date: 2014
Publisher: Beijing Institute of Aerospace Information
Source: Journal of systems engineering and electronics, 2014, v. 25, no. 6, p. 1089-1097 How to cite?
Journal: Journal of Systems Engineering and Electronics 
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.
URI: http://hdl.handle.net/10397/21019
ISSN: 1004-4132
DOI: 10.1109/JSEE.2014.00125
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

2
Last Week
0
Last month
0
Citations as of Aug 17, 2017

Page view(s)

61
Last Week
5
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.