Distributed community detection in social networks with genetic algorithms

Community detection in social networks is a hot research topic that has received great interest in the recent years due to its wide applicability. This paper proposes a scalable approach for community structure identification using a genetic algorithm. Two existing fitness functions are analyzed and...

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Bibliographic Details
Published in2010 IEEE International Conference on Intelligent Computer Communication and Processing pp. 35 - 41
Main Authors Halalai, Raluca, Lemnaru, Camelia, Potolea, Rodica
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2010
Subjects
Clustering algorithms
Communities
Educational institutions
Genetics
Libraries
Scalability
Social network services
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ISBN9781424482283
1424482283
DOI10.1109/ICCP.2010.5606467

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Summary:Community detection in social networks is a hot research topic that has received great interest in the recent years due to its wide applicability. This paper proposes a scalable approach for community structure identification using a genetic algorithm. Two existing fitness functions are analyzed and genetic parameters are tuned on thoroughly studied networks with known community structures. Experiments on a large data set show how the amount of time necessary to determine meaningful communities in a network is significantly reduced by running the algorithm distributed. This enables the analysis of larger, real-world networks. We then propose a new fitness function that offers a good tradeoff between efficiency and speed.
ISBN:9781424482283
1424482283
DOI:10.1109/ICCP.2010.5606467