SiFSO: fish swarm optimization-based technique for efficient community detection in complex networks

Yasir Ahmad; Mohib Ullah; Rafiullah Khan; Bushra Shafi; Atif Khan; Mahdi Zareei; Abdallah Aldosary; Ehab Mahmoud Mohamed

doi:10.1155/2020/6695032

SiFSO: fish swarm optimization-based technique for efficient community detection in complex networks

Yasir Ahmad, Mohib Ullah, Rafiullah Khan, Bushra Shafi, Atif Khan, Mahdi Zareei, Abdallah Aldosary^*, Ehab Mahmoud Mohamed

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

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Abstract

Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This studyproposed the Sigmoid Fish Swarm Optimization (SiFSO) algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The proposed SiFSO algorithm's performance is tested against state-of-the-art particle swarm optimization (PSO) algorithms in Q-modularity and normalized mutual information (NMI). The results showed that the proposed SiFSO algorithm is 0.0014% better in terms of Q-modularity and 0.1187% better in terms of NMI than the other selected algorithms.

Original language	English
Article number	6695032
Pages (from-to)	1-9
Number of pages	9
Journal	Complexity
Volume	2020
Issue number	1
DOIs	https://doi.org/10.1155/2020/6695032
Publication status	Published - 2020
Externally published	Yes

Bibliographical note

Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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10.1155/2020/6695032Licence: CC BY

Publisher version (open access)Final published version, 3.68 MBLicence: CC BY

Cite this

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title = "SiFSO: fish swarm optimization-based technique for efficient community detection in complex networks",

abstract = "Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This studyproposed the Sigmoid Fish Swarm Optimization (SiFSO) algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The proposed SiFSO algorithm's performance is tested against state-of-the-art particle swarm optimization (PSO) algorithms in Q-modularity and normalized mutual information (NMI). The results showed that the proposed SiFSO algorithm is 0.0014\% better in terms of Q-modularity and 0.1187\% better in terms of NMI than the other selected algorithms.",

author = "Yasir Ahmad and Mohib Ullah and Rafiullah Khan and Bushra Shafi and Atif Khan and Mahdi Zareei and Abdallah Aldosary and Mohamed, \{Ehab Mahmoud\}",

note = "Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.",

year = "2020",

doi = "10.1155/2020/6695032",

language = "English",

volume = "2020",

pages = "1--9",

journal = "Complexity",

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publisher = "John Wiley \& Sons",

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TY - JOUR

T1 - SiFSO

T2 - fish swarm optimization-based technique for efficient community detection in complex networks

AU - Ahmad, Yasir

AU - Ullah, Mohib

AU - Khan, Rafiullah

AU - Shafi, Bushra

AU - Khan, Atif

AU - Zareei, Mahdi

AU - Aldosary, Abdallah

AU - Mohamed, Ehab Mahmoud

N1 - Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2020

Y1 - 2020

N2 - Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This studyproposed the Sigmoid Fish Swarm Optimization (SiFSO) algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The proposed SiFSO algorithm's performance is tested against state-of-the-art particle swarm optimization (PSO) algorithms in Q-modularity and normalized mutual information (NMI). The results showed that the proposed SiFSO algorithm is 0.0014% better in terms of Q-modularity and 0.1187% better in terms of NMI than the other selected algorithms.

AB - Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This studyproposed the Sigmoid Fish Swarm Optimization (SiFSO) algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The proposed SiFSO algorithm's performance is tested against state-of-the-art particle swarm optimization (PSO) algorithms in Q-modularity and normalized mutual information (NMI). The results showed that the proposed SiFSO algorithm is 0.0014% better in terms of Q-modularity and 0.1187% better in terms of NMI than the other selected algorithms.

UR - https://www.scopus.com/pages/publications/85098186708

U2 - 10.1155/2020/6695032

DO - 10.1155/2020/6695032

M3 - Article

SN - 1076-2787

VL - 2020

SP - 1

EP - 9

JO - Complexity

JF - Complexity

IS - 1

M1 - 6695032

ER -

SiFSO: fish swarm optimization-based technique for efficient community detection in complex networks

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