Identifying communities in complex networks using learning-based genetic algorithm
Identifying communities is one of the hardest tasks in network analysis, and it is critical in various fields, including computer science, biology, sociology, and physics. It aims to partition the graph of a network into groups/clusters of nodes (communities) according to the graph topology. Because...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447924004064 |
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| author | Gholam Reza Abdi Amir Hosein Refahi Sheikhani Sohrab Kordrostami Bagher Zarei Mohsen Falah Rad |
| author_facet | Gholam Reza Abdi Amir Hosein Refahi Sheikhani Sohrab Kordrostami Bagher Zarei Mohsen Falah Rad |
| author_sort | Gholam Reza Abdi |
| collection | DOAJ |
| description | Identifying communities is one of the hardest tasks in network analysis, and it is critical in various fields, including computer science, biology, sociology, and physics. It aims to partition the graph of a network into groups/clusters of nodes (communities) according to the graph topology. Because determining the optimal partition is a computationally difficult task, it is usually carried out using optimization methods. Most optimization methods proposed for this problem have considered network modularity as the objective function. This article proposes a new evolutionary algorithm called LGA to tackle the community detection problem through modularity optimization. In LGA, learning automata are utilized in the evolution process of the genetic algorithm. Utilizing learning automata in the evolution process of the genetic algorithm largely prevents getting stuck in local optima and premature convergence of the genetic algorithm. It has been tested on different examples of the community detection problem to assess the performance of the LGA. The experiment results showed that the LGA efficiently detects communities within networks. On average, its performance is 26.47% better in real-world networks and 48.32% better in synthetic networks than in compared algorithms. |
| format | Article |
| id | doaj-art-3a23dcb983ee4e4abe7b03eaacc6f7ed |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-3a23dcb983ee4e4abe7b03eaacc6f7ed2024-12-18T08:48:12ZengElsevierAin Shams Engineering Journal2090-44792024-12-011512103031Identifying communities in complex networks using learning-based genetic algorithmGholam Reza Abdi0Amir Hosein Refahi Sheikhani1Sohrab Kordrostami2Bagher Zarei3Mohsen Falah Rad4Department of Applied Mathematics and Computer Science, Lahijan Branch, Islamic Azad University, Lahijan, IranDepartment of Applied Mathematics and Computer Science, Lahijan Branch, Islamic Azad University, Lahijan, Iran; Corresponding author.Department of Applied Mathematics and Computer Science, Lahijan Branch, Islamic Azad University, Lahijan, IranDepartment of Computer Engineering and Information Technology, Shabestar Branch, Islamic Azad University, Shabestar, IranDepartment of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, IranIdentifying communities is one of the hardest tasks in network analysis, and it is critical in various fields, including computer science, biology, sociology, and physics. It aims to partition the graph of a network into groups/clusters of nodes (communities) according to the graph topology. Because determining the optimal partition is a computationally difficult task, it is usually carried out using optimization methods. Most optimization methods proposed for this problem have considered network modularity as the objective function. This article proposes a new evolutionary algorithm called LGA to tackle the community detection problem through modularity optimization. In LGA, learning automata are utilized in the evolution process of the genetic algorithm. Utilizing learning automata in the evolution process of the genetic algorithm largely prevents getting stuck in local optima and premature convergence of the genetic algorithm. It has been tested on different examples of the community detection problem to assess the performance of the LGA. The experiment results showed that the LGA efficiently detects communities within networks. On average, its performance is 26.47% better in real-world networks and 48.32% better in synthetic networks than in compared algorithms.http://www.sciencedirect.com/science/article/pii/S2090447924004064Network analysisCommunity detectionModularity optimizationEvolutionary algorithmGenetic algorithmLearning automata |
| spellingShingle | Gholam Reza Abdi Amir Hosein Refahi Sheikhani Sohrab Kordrostami Bagher Zarei Mohsen Falah Rad Identifying communities in complex networks using learning-based genetic algorithm Ain Shams Engineering Journal Network analysis Community detection Modularity optimization Evolutionary algorithm Genetic algorithm Learning automata |
| title | Identifying communities in complex networks using learning-based genetic algorithm |
| title_full | Identifying communities in complex networks using learning-based genetic algorithm |
| title_fullStr | Identifying communities in complex networks using learning-based genetic algorithm |
| title_full_unstemmed | Identifying communities in complex networks using learning-based genetic algorithm |
| title_short | Identifying communities in complex networks using learning-based genetic algorithm |
| title_sort | identifying communities in complex networks using learning based genetic algorithm |
| topic | Network analysis Community detection Modularity optimization Evolutionary algorithm Genetic algorithm Learning automata |
| url | http://www.sciencedirect.com/science/article/pii/S2090447924004064 |
| work_keys_str_mv | AT gholamrezaabdi identifyingcommunitiesincomplexnetworksusinglearningbasedgeneticalgorithm AT amirhoseinrefahisheikhani identifyingcommunitiesincomplexnetworksusinglearningbasedgeneticalgorithm AT sohrabkordrostami identifyingcommunitiesincomplexnetworksusinglearningbasedgeneticalgorithm AT bagherzarei identifyingcommunitiesincomplexnetworksusinglearningbasedgeneticalgorithm AT mohsenfalahrad identifyingcommunitiesincomplexnetworksusinglearningbasedgeneticalgorithm |