Community detection using genetic algorithm under a novel background

Pao Yue-kong Library Electronic Theses Database

Community detection using genetic algorithm under a novel background

 

Author: He, Tiantian
Title: Community detection using genetic algorithm under a novel background
Degree: M.Sc.
Year: 2012
Subject: Social media.
Online social networks.
Internet -- Social aspects.
World Wide Web -- Social aspects.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Computing
Pages: vii, 53 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2473666
URI: http://theses.lib.polyu.edu.hk/handle/200/6418
Abstract: As an extension of social communication, social network in cyber world plays an important role in people's modern life. Like solid relationship networks, social networks also have some distinguished structures such as groups or communities. In these communities, people prefer to contact with those who are in the same one rather than do with those who belong to the different. However, from a macro perspective, a social network appears its feature of disorder and it brings much inconvenience to the analysis and further research. In order to identify the structure of the social network or other complex networks, numbers of scholars plunge much effort into this research field and it becomes a hot-spot at current, which is named as Community Detection. In this paper, a novel approach to detecting community structure is proposed. Unlike previous theories which concern topological metrics as a sole factor having effect on the constitution of community, the algorithm proposed in this paper considers both topological metrics and the practical meaning of each vertex and connection in a social network as perspectives affecting community structure. In other words, we concern more about interactions led by the peculiarity people possess, and then the community is constituted by those people who possess such meaningful interactions. Through being tested by different evaluating metrics and compared to other prevalent approaches, our algorithm shows its effectiveness on Community Detection.

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