A REVIEW ON OVERLAPPING AND NON-OVERLAPPING COMMUNITY DETECTION ALGORITHMS FOR SOCIAL NETWORK ANALYTICS

Edi surya negara, edi and Ria andriani, Ria (2018) A REVIEW ON OVERLAPPING AND NON-OVERLAPPING COMMUNITY DETECTION ALGORITHMS FOR SOCIAL NETWORK ANALYTICS. Far East Journal of Electronics and Communications, 18 (1). pp. 1-27. ISSN 0973-7006

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Official URL: http://dx.doi.org/10.17654/EC018010001

Abstract

Community detection is a common problem that exists in the data graph analytics as the social networks analytics. In the context of social networks, community detection is aimed at to find a group or community that has connectedness between individuals (nodes) with high-intensity interaction. In general, types of group on the social networking can be divided into the overlapping and non-overlapping. We provide an overview of some of the algorithms of overlapping and non-overlapping community detections available today to perform an analysis or a breakdown of the data of social networking. The algorithms for overlapping community detection are: (1) local seed selection algorithm; (2) seed set expansion algorithm; (3) speaker listener label propagation algorithm (SPLA) and the algorithms for detection of non-overlapping community are: (4) multithreaded

Item Type: Article
Additional Information: e.s.negara@binadarma.ac.id
Uncontrolled Keywords: community detection, data graph analytic, data mining, graph clustering, social network analytics
Subjects: A General Works > AI Indexes (General)
Divisions: Faculty of Law, Arts and Social Sciences > School of Humanities
Depositing User: Mr Priyono Pri
Date Deposited: 21 Mar 2018 06:31
Last Modified: 20 Aug 2018 01:21
URI: http://eprints.binadarma.ac.id/id/eprint/3692

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