Abstract
In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity.
Highlights
The analysis of complex networks in real-world applications such as social, biological, metabolic, and paper citation networks is receiving more attention from researchers and experts [1,2]
The solution vectors representation, the distance calculation, and the spiral flight movement of the moth–flame optimization (MFO) algorithm were adapted for community detection
The performance of the discrete moth–flame optimization algorithm for community detection (DMFO-Community detection (CD)) was experimentally evaluated on eleven real-world networks and compared with five well-known algorithms in community detection in terms of modularity, normalized mutual information (NMI), and the number of detected communities
Summary
The analysis of complex networks in real-world applications such as social, biological, metabolic, and paper citation networks is receiving more attention from researchers and experts [1,2]. An important issue in most real-world networks is to find the hidden structures. Community detection (CD) identifies these structures of a complex network, and the density of edges inside these structures is higher than their outside. The more similarity between the members of a community has been caused the community detection able to be used as a tool in the analysis of complex networks structure [3]. CD has a significant role in social network analysis, which includes the identification of friendship groups, relationship analysis, identify influential people, detect terrorist attacks, use in link-prediction, or identify classes in COVID-19 datasets [1,4,5]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
