Dynamic Community in static social networks using the gray wolf optimizer algorithm
Identifying communities in complex networks is an important issues in social network analysis, and it helps researchers understand the function and display of network structures. Clustering or recognizing communities will reveal the structure of groups in social networks and hidden communication bet...
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University of Qom
2020-03-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_1267_2adbe4e6a45159c250534efabd54df6a.pdf |
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| _version_ | 1832577576902590464 |
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| author | Fatemeh Besharatnia Alireza Talebpur Sadegh Aliakbari |
| author_facet | Fatemeh Besharatnia Alireza Talebpur Sadegh Aliakbari |
| author_sort | Fatemeh Besharatnia |
| collection | DOAJ |
| description | Identifying communities in complex networks is an important issues in social network analysis, and it helps researchers understand the function and display of network structures. Clustering or recognizing communities will reveal the structure of groups in social networks and hidden communication between its components. A community is a collection of nodes whose density of communication is more than the other network entities.In this paper, a new algorithm for recognizing communities in static networks has been presented which utilizes Gray Wolf Optimizer algorithm, which has the ability to scale according to the selected criteria. It has been shown that one of the most important characteristics of meta-algorithms is the lack of trapping at the local minimum. Gray Wolf Optimizer algorithm is less likely to be trapped than other optimization algorithms such as the genetic algorithm and the particle swarm algorithm. Finally, the results of the experiments showed that the algorithm is better than other algorithms on average. |
| format | Article |
| id | doaj-art-2b80550b78854789bcd7f57157a72e07 |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2020-03-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-2b80550b78854789bcd7f57157a72e072025-01-30T20:17:18ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752020-03-016113114310.22091/jemsc.2018.12671267Dynamic Community in static social networks using the gray wolf optimizer algorithmFatemeh Besharatnia0Alireza Talebpur1Sadegh Aliakbari2دانشجویی دکتری دانشگاه شهید بهشتی، دانشکده علوم و مهندسی کامپیوتردانشیار دانشگاه شهید بهشتی، دانشکده علوم و مهندسی کامپیوتراستادیار دانشگاه شهید بهشتی، دانشکده علوم و مهندسی کامپیوترIdentifying communities in complex networks is an important issues in social network analysis, and it helps researchers understand the function and display of network structures. Clustering or recognizing communities will reveal the structure of groups in social networks and hidden communication between its components. A community is a collection of nodes whose density of communication is more than the other network entities.In this paper, a new algorithm for recognizing communities in static networks has been presented which utilizes Gray Wolf Optimizer algorithm, which has the ability to scale according to the selected criteria. It has been shown that one of the most important characteristics of meta-algorithms is the lack of trapping at the local minimum. Gray Wolf Optimizer algorithm is less likely to be trapped than other optimization algorithms such as the genetic algorithm and the particle swarm algorithm. Finally, the results of the experiments showed that the algorithm is better than other algorithms on average.https://jemsc.qom.ac.ir/article_1267_2adbe4e6a45159c250534efabd54df6a.pdfsocial networkscommunity detectionmeta-algorithmsgray wolf optimizer algorithm |
| spellingShingle | Fatemeh Besharatnia Alireza Talebpur Sadegh Aliakbari Dynamic Community in static social networks using the gray wolf optimizer algorithm مدیریت مهندسی و رایانش نرم social networks community detection meta-algorithms gray wolf optimizer algorithm |
| title | Dynamic Community in static social networks using the gray wolf optimizer algorithm |
| title_full | Dynamic Community in static social networks using the gray wolf optimizer algorithm |
| title_fullStr | Dynamic Community in static social networks using the gray wolf optimizer algorithm |
| title_full_unstemmed | Dynamic Community in static social networks using the gray wolf optimizer algorithm |
| title_short | Dynamic Community in static social networks using the gray wolf optimizer algorithm |
| title_sort | dynamic community in static social networks using the gray wolf optimizer algorithm |
| topic | social networks community detection meta-algorithms gray wolf optimizer algorithm |
| url | https://jemsc.qom.ac.ir/article_1267_2adbe4e6a45159c250534efabd54df6a.pdf |
| work_keys_str_mv | AT fatemehbesharatnia dynamiccommunityinstaticsocialnetworksusingthegraywolfoptimizeralgorithm AT alirezatalebpur dynamiccommunityinstaticsocialnetworksusingthegraywolfoptimizeralgorithm AT sadeghaliakbari dynamiccommunityinstaticsocialnetworksusingthegraywolfoptimizeralgorithm |