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Community Detection in Social Network Using Artificial Bee Colony with Genetic Operator

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dc.contributor.author Aung, Thet Thet
dc.contributor.author Nyunt, Thi Thi Soe
dc.date.accessioned 2019-08-05T04:28:50Z
dc.date.available 2019-08-05T04:28:50Z
dc.date.issued 2018-11-01
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1737
dc.description.abstract Community detection (CD) plays an important role in analyzing social network features and helping to find out valuable hidden information. Many research algorithms have been proposed to find the best community in the network. But it has many challenges such as scalability and time complexity. This paper proposes a new algorithm, Artificial Bee Colony Algorithm with Genetic Operator (ABCGO) that combines crossover and mutation operators with Artificial Bee Colony algorithm. This paper takes modularity Q as objective function. Compared with five state-of-art algorithms, results on real world networks reflect the effectiveness of ABCGO en_US
dc.language.iso en en_US
dc.publisher Second International Conference on Advanced Information Technologies (ICAIT 2018) en_US
dc.subject Social Network en_US
dc.subject Community Detection en_US
dc.subject Artificial Bee Colony en_US
dc.subject Modularity en_US
dc.title Community Detection in Social Network Using Artificial Bee Colony with Genetic Operator en_US
dc.type Article en_US


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