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Reseach Article

Survey on Community Detection in Online Social Networks

by Amit Dhumal, Pravin Kamde
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 9
Year of Publication: 2015
Authors: Amit Dhumal, Pravin Kamde
10.5120/21571-4609

Amit Dhumal, Pravin Kamde . Survey on Community Detection in Online Social Networks. International Journal of Computer Applications. 121, 9 ( July 2015), 35-41. DOI=10.5120/21571-4609

@article{ 10.5120/21571-4609,
author = { Amit Dhumal, Pravin Kamde },
title = { Survey on Community Detection in Online Social Networks },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 9 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 35-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number9/21571-4609/ },
doi = { 10.5120/21571-4609 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:08:02.419041+05:30
%A Amit Dhumal
%A Pravin Kamde
%T Survey on Community Detection in Online Social Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 9
%P 35-41
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The proposed survey discusses the topic of community detection in the context of online social network. Community detection helps to identify link density within network structure and to predict future missing links. In online social networks, nodes typically represent individuals and edges indicate relationships between them. Due to the complexity, dynamic nature and huge scale of network, community detection in online social networks is challenging task. In this survey various community detection methods for networks with static and dynamic nature are discussed, and results of applying them on online social network is are provided.

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Index Terms

Computer Science
Information Sciences

Keywords

Community detection online social networks data clustering