NDLI: A method to detect communities with stability in social networks

Content Provider	Springer Nature Link
Author	Nguyen, Nam P. Alim, Md Abdul Dinh, Thang N. Thai, My T.
Copyright Year	2014
Abstract	We propose stable community detection (SCD), a framework to effectively identify stable communities in online social networks (OSNs). Our framework works by first enriching the input network with the mutual relationship estimation of all links and then discovering stable communities using a lumped Markov chain model. SCD has the advantage of handling the real model of OSNs with weighted reciprocity relationships. This approach is also supported by a key connection between the persistence probability of a community and its local topology.To certify the efficiency and stability of the discovered communities, we test SCD on both synthesized datasets and real-world social traces, including the NetHEPT collaboration, Foursquare, Twitter and Facebook social networks, in reference to the consensus of other state-of-the-art detection methods. Competitive experimental results confirm the quality and efficacy of the proposed framework on identifying stable communities in OSNs.
Starting Page	1
Ending Page	15
Page Count	15
File Format	PDF
ISSN	18695450
Journal	Social Network Analysis and Mining
Volume Number	4
Issue Number	1
e-ISSN	18695469
Language	English
Publisher	Springer Vienna
Publisher Date	2014-08-10
Publisher Place	Vienna
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Community detection Online social network Stable community detection Lumped Markov chain Data Mining and Knowledge Discovery Complex Networks Game Theory, Economics, Social and Behav. Sciences Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Methodology of the Social Sciences
Content Type	Text
Resource Type	Article
Subject	Information Systems Human-Computer Interaction Communication Computer Science Applications Media Technology

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