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Comparative analysis of community discovery methods in social networks
Author(s) -
M. Mohamed Sathik,
K. Senthamarai Kannan,
A. Abdul Rasheed
Publication year - 2011
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/1902-2536
Subject(s) - popularity , computer science , partition (number theory) , data science , globe , social network analysis , field (mathematics) , the internet , world wide web , social network (sociolinguistics) , set (abstract data type) , community structure , social media , medicine , psychology , social psychology , mathematics , combinatorics , pure mathematics , ophthalmology , programming language
study of networks is an active area of research due to its capability of modeling many real world complex systems. Social Network gains its popularity due to its ease of use. It enables people all over the world to interact with each other with the advent of Web 2.0 in this Internet era. Online Social Networking facilitates people to have communication nevertheless of considering geographical location over the globe. Social Network Analysis is the field of research that provides a set of tools and theoretical approaches for holistic exploration of the communication and interaction patterns of social systems. A common pattern among the group of people in a network is considered as a community which is a partition of the entire network structure. There are few existing methods for discovering communities. We introduced a method called "mutual accessibility" for community discovery. This article compares such three different methods including the one that we introduced. The results of those methods are also shown by taking various datasets as an analysis.

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