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A Perspective Analysis of Hidden Community Mining Methods in Large Scale Social Networks

by Renuga Devi. R, Hemalatha. M
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 75 - Number 3
Year of Publication: 2013
Authors: Renuga Devi. R, Hemalatha. M
10.5120/13089-0368

Renuga Devi. R, Hemalatha. M . A Perspective Analysis of Hidden Community Mining Methods in Large Scale Social Networks. International Journal of Computer Applications. 75, 3 ( August 2013), 7-12. DOI=10.5120/13089-0368

@article{ 10.5120/13089-0368,
author = { Renuga Devi. R, Hemalatha. M },
title = { A Perspective Analysis of Hidden Community Mining Methods in Large Scale Social Networks },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 75 },
number = { 3 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 7-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume75/number3/13089-0368/ },
doi = { 10.5120/13089-0368 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:43:15.789751+05:30
%A Renuga Devi. R
%A Hemalatha. M
%T A Perspective Analysis of Hidden Community Mining Methods in Large Scale Social Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 75
%N 3
%P 7-12
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Research in social network analysis has increased in recent years. Because of the popularity of the social networking sites, many researchers concentrate on this area for research. In this, community mining plays an important role. Hidden communities affect the social networks in different ways. But not all hidden communities are dangerous or illegal. Most of the hidden communities are having potential knowledge. Communities are represented as a graph format. People are represented as nodes, and the relationship between the nodes are represented as edges. Several mining techniques do not considered the disconnected edges in the graph. Those hidden or disconnected edges may useful to the others in the network. Our approach on social network is fully based on the community mining on heterogeneous network. Here we analysis the various community mining techniques which is already available. Such as MinCut algorithm, Regression based algorithm, Max-Min modularity measure, LM algorithm and SECI model. Our results show that, there are some limitations in the hidden community mining technique in large scale networks. So we planned to do research in this area for better improvement.

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Index Terms

Computer Science
Information Sciences

Keywords

Social networks Community Mining Hidden Communities Disconnected edges mining techniques

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