A New Meta-heuristic Algorithm based on Multi-criteria Decision Making to Solve Community Detection Problem
Community detection is one of the most significant issues in the field of social networks. The main purpose of community detection is to partition the network in such a way that the relations between components of the network are dense. Because of the strong relations among network members in these...
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| Format: | Article |
| Language: | English |
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University of Tehran
2018-06-01
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| Series: | Journal of Information Technology Management |
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| Online Access: | https://jitm.ut.ac.ir/article_63113_32b7d7223b0fe80e70cd49eeb2385757.pdf |
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| author | Vahid Baradaran Amir Hossein Hosseinian Reza Derakhshani |
| author_facet | Vahid Baradaran Amir Hossein Hosseinian Reza Derakhshani |
| author_sort | Vahid Baradaran |
| collection | DOAJ |
| description | Community detection is one of the most significant issues in the field of social networks. The main purpose of community detection is to partition the network in such a way that the relations between components of the network are dense. Because of the strong relations among network members in these partitions, you can consider them as a community. Many researchers have developed several algorithms to solve such a problem. Therefore, we present a genetic algorithm based on Topsis which is a multi-criteria decision making method (MCDM). The proposed algorithm uses Topsis to rank solutions based on modularity and modularity density which are two of the most well-known criteria in community detection problem. Thereafter, crossover and mutation operators are only applied on solutions ranked by Topsis. The performance of the proposed algorithm has been evaluated through comparing it against classical genetic algorithm and a greedy one. The results showed that the proposed algorithm outperforms the other two methods. Since the application of MCDM approach has not been reported in the related literature, this paper can be considered as a basis for future studies. |
| format | Article |
| id | doaj-art-d7a6fb330b6d4ffca965def266b4b1ab |
| institution | OA Journals |
| issn | 2008-5893 2423-5059 |
| language | English |
| publishDate | 2018-06-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj-art-d7a6fb330b6d4ffca965def266b4b1ab2025-08-20T02:20:59ZengUniversity of TehranJournal of Information Technology Management2008-58932423-50592018-06-0110228330810.22059/jitm.2017.223145.189663113A New Meta-heuristic Algorithm based on Multi-criteria Decision Making to Solve Community Detection ProblemVahid Baradaran0Amir Hossein Hosseinian1Reza Derakhshani2Assistant Prof. of Industrial Engineering, Islamic Azad University, North Tehran Branch, Tehran, IranPh.D. Candidate of Industrial Engineering, Islamic Azad University North Tehran Branch, Tehran, IranPh.D. Candidate of Industrial Engineering, Islamic Azad University North Tehran Branch, Tehran, IranCommunity detection is one of the most significant issues in the field of social networks. The main purpose of community detection is to partition the network in such a way that the relations between components of the network are dense. Because of the strong relations among network members in these partitions, you can consider them as a community. Many researchers have developed several algorithms to solve such a problem. Therefore, we present a genetic algorithm based on Topsis which is a multi-criteria decision making method (MCDM). The proposed algorithm uses Topsis to rank solutions based on modularity and modularity density which are two of the most well-known criteria in community detection problem. Thereafter, crossover and mutation operators are only applied on solutions ranked by Topsis. The performance of the proposed algorithm has been evaluated through comparing it against classical genetic algorithm and a greedy one. The results showed that the proposed algorithm outperforms the other two methods. Since the application of MCDM approach has not been reported in the related literature, this paper can be considered as a basis for future studies.https://jitm.ut.ac.ir/article_63113_32b7d7223b0fe80e70cd49eeb2385757.pdfCommunity detectionGenetic algorithmOptimizationSocial networksTOPSIS |
| spellingShingle | Vahid Baradaran Amir Hossein Hosseinian Reza Derakhshani A New Meta-heuristic Algorithm based on Multi-criteria Decision Making to Solve Community Detection Problem Journal of Information Technology Management Community detection Genetic algorithm Optimization Social networks TOPSIS |
| title | A New Meta-heuristic Algorithm based on
Multi-criteria Decision Making to Solve
Community Detection Problem |
| title_full | A New Meta-heuristic Algorithm based on
Multi-criteria Decision Making to Solve
Community Detection Problem |
| title_fullStr | A New Meta-heuristic Algorithm based on
Multi-criteria Decision Making to Solve
Community Detection Problem |
| title_full_unstemmed | A New Meta-heuristic Algorithm based on
Multi-criteria Decision Making to Solve
Community Detection Problem |
| title_short | A New Meta-heuristic Algorithm based on
Multi-criteria Decision Making to Solve
Community Detection Problem |
| title_sort | new meta heuristic algorithm based on multi criteria decision making to solve community detection problem |
| topic | Community detection Genetic algorithm Optimization Social networks TOPSIS |
| url | https://jitm.ut.ac.ir/article_63113_32b7d7223b0fe80e70cd49eeb2385757.pdf |
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