A review: Multi-Objective Algorithm for Community Detection in Complex Social Networks
Recently, research on multi-objective optimization algorithms for community detection in complex networks has grown considerably. Community detection based on multi-objective algorithms (MOAs) in complex social networks is a fundamental scheduler, and it supports knowing the dynamics of a society, f...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Human Development
2025-02-01
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| Series: | UHD Journal of Science and Technology |
| Subjects: | |
| Online Access: | https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1405 |
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| Summary: | Recently, research on multi-objective optimization algorithms for community detection in complex networks has grown considerably. Community detection based on multi-objective algorithms (MOAs) in complex social networks is a fundamental scheduler, and it supports knowing the dynamics of a society, finding influential groups, and improving information dissemination. The traditional methodologies often cannot cope with the features that real-world network usually present, related to optimizing various and sometimes conflicting objectives. This paper provides an overview of some recent works on MOAs for community detection in complex social networks. This paper will explore the balance of the reached objectives, such as modularity, community size, and edge density. Which are analyzed by 15 different approaches in order to choose from works published during the period 2019–2024. These strengths and limitations of various MOAs are reviewed with a comparative analysis to provide insights into both the effectiveness and computational efficiency of these methods. The present trends and future research are discussed that underline the need for the development of solutions to be more adaptive and scalable in coping with the gradually increasing complexity of social networks. |
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| ISSN: | 2521-4209 2521-4217 |