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Title

social network clustering with genetic algorithm

Year: 1400
COI: ICIRES09_012
Language: EnglishView: 81
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Authors

Sara Khojasteh - Department Of Computer Engineering Apadana Institute Of Higher Education Shiraz, Iran
Pirooz Shamsinejad Babaki - Department of Computer engineering and information technology Shiraz university of technology Shiraz,Iran
Haleh Homayouni - Department Of Computer Engineering Apadana Institute Of Higher Education Shiraz, Iran

Abstract:

Many people today establish part of their relationship with friends through virtual social networks. One of the most practical issues in computer science is the issue of data clustering, which has many applications in the field of social networking, pattern finding, and data similarity. Many researchers in various fields have done various researches about it. On the other hand, the possibility of modeling many problems has caused widespread attention to graph clustering. Since single-objective optimization algorithms can not optimize all the objectives of community discovery, in this research, a two-objective meta-heuristic algorithm is proposed for this purpose. Researchers have used several genetic algorithms to identify communities, but the proposed algorithm uses two goals together, which form the basis of defining communities, which improves efficiency and accuracy. The performance results of the proposed method are compared with other genetic-based algorithms by standard data sets in the field of social network analysis and the results show the superiority of the proposed method over other methods.

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This Paper COI Code is ICIRES09_012. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

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Khojasteh, Sara and Shamsinejad Babaki, Pirooz and Homayouni, Haleh,1400,social network clustering with genetic algorithm,9th International Conference on Innovation and Research in Engineering Sciences,https://civilica.com/doc/1257677

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Type of center: موسسه غیرانتفاعی
Paper count: 1,196
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