Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26317
Title: An approach for hesitant node classification in overlapping community detection
Authors: Wang, J
Peng, J
Liu, O 
Keywords: Community detection
Density
Hesitant node
Rough set
Trust path
Issue Date: 2014
Publisher: Pacific Asia Conference on Information Systems
Source: Proceedings - Pacific Asia Conference on Information Systems, PACIS 2014, 2014 How to cite?
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.
URI: http://hdl.handle.net/10397/26317
Appears in Collections:Conference Paper

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

Page view(s)

58
Last Week
3
Last month
Checked on Sep 24, 2017

Google ScholarTM

Check



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