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Community Detection in Social Graph Using Nature-Inspired Based Artificial Bee Colony Algorithm with Crossover and Mutation

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dc.contributor.author Aung, Thet Thet
dc.contributor.author Nyunt, Thi Thi Soe
dc.contributor.author Cho, Pyae Pyae Win
dc.date.accessioned 2019-08-05T06:38:40Z
dc.date.available 2019-08-05T06:38:40Z
dc.date.issued 2019
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1746
dc.description.abstract Many types of social network are modelled as graphs. Community detection has been an important research area in social graph analysis. Community detection can be viewed as an optimization problem. Nowadays, researchers use nature-inspired algorithms to solve optimization problem. Their goal is to find the optimal solution for a given problem. In this paper, nature-inspired based artificial bee colony algorithm with crossover and mutation is used to detect community in social graphs. GraphX is built as a library on the top of Spark by encoding graph as a collection of vertices and edges. Comparative studies describe that the proposed algorithm and other nature-inspired algorithms can effectively detect the community structure on real world social graphs as other traditional community detection algorithms. en_US
dc.language.iso en en_US
dc.publisher IEEE 4th International Conference on Computer and Communication Systems(2019) en_US
dc.subject artificial bee colony en_US
dc.subject community detection en_US
dc.subject graph en_US
dc.subject GraphX en_US
dc.subject spark en_US
dc.subject crossover en_US
dc.subject mutation and modularity en_US
dc.title Community Detection in Social Graph Using Nature-Inspired Based Artificial Bee Colony Algorithm with Crossover and Mutation en_US
dc.type Article en_US


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