Cited By
View all- Fozuni Shirjini MFarzi SNikanjam A(2020)MDPCluster: a swarm-based community detection algorithm in large-scale graphsComputing10.1007/s00607-019-00787-4102:4(893-922)Online publication date: 11-Jan-2020
AFSMA: An Enhanced Artificial Fish Swarm Algorithm Based on Mutuality for Community Detection
Authors: 
Yixin Wang, Wanyue Xu, Qinma KangAuthors Info & Claims
Yixin Wang, Wanyue Xu, Qinma KangAuthors Info & ClaimsPages 107 - 113
Abstract
Community structure is an important property of complex networks. Detecting these communities is of great significance to a wide range of applications. Community detection is an NP-hard problem having received great attentions in recent years. Modularity Q is by far the most common and well-known fitness function for measuring the quality of network division. Many optimization algorithms have been developed for community detection. In this paper, we propose a new modularity optimization method based on the artificial fish swarm algorithm, namely AFSMA. In the algorithm, mutuality is defined to represent the distance and relationship between nodes. A series of artificial fish swarm's behaviors are used to simulate the change of nodes' community labels. Experimental results on datasets of multiple real-world networks have shown the viability and effectiveness of the proposed algorithm.
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- AFSMA: An Enhanced Artificial Fish Swarm Algorithm Based on Mutuality for Community Detection
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October 2018
221 pages
ISBN:9781450364768
DOI:10.1145/3291801
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- Shandong Univ.: Shandong University
- University of Queensland: University of Queensland
- Dalian Maritime University
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Published: 27 October 2018
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ICBDR 2018
ICBDR 2018: 2018 The 2nd International Conference on Big Data Research
October 27 - 29, 2018
Weihai, China
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View all- Fozuni Shirjini MFarzi SNikanjam A(2020)MDPCluster: a swarm-based community detection algorithm in large-scale graphsComputing10.1007/s00607-019-00787-4102:4(893-922)Online publication date: 11-Jan-2020
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