research-article

CC-GA: : A clustering coefficient based genetic algorithm for detecting communities in social networks

Authors:

Rabeeh Ayaz Abbasi,

Onaiza Maqbool,

Naif Radi AljohaniAuthors Info & Claims

Volume 63, Issue C

Pages 59 - 70

https://doi.org/10.1016/j.asoc.2017.11.014

Published: 01 February 2018 Publication History

Highlights

•

Using clustering coefficient for initial population results in high modularity.

•

Proposed community structure based mutation allows fast convergence.

•

CC-GA produces competitive results to nine existing algorithms on various networks.

Abstract

A community structure is an integral part of a social network. Detecting such communities plays an important role in a wide range of applications, including but not limited to cluster analysis, recommendation systems and understanding the behaviour of complex systems. Researchers have derived many algorithms to discover the community structures of networks. Discovering communities is a challenging task, and there is no single algorithm that produces the best results for all networks. Therefore, despite many elegant solutions, discovering communities remains an active area of research. In this paper, we propose a novel algorithm, the Clustering Coefficient-based Genetic Algorithm (CC-GA), for detecting them in social and complex networks. Researchers have used several genetic algorithms to detect communities, but the proposed algorithm is novel in terms of both the generation of the initial population and the mutation method, and these improve its efficiency and accuracy. Experiments on a variety of real-world datasets and a comparison to state-of-the-art genetic and non-genetic-based algorithms show improved results.

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Index Terms

CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 63, Issue C

Feb 2018

306 pages

ISSN:1568-4946

Issue’s Table of Contents

Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 February 2018

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https://dl.acm.org/doi/10.1007/s11227-023-05852-6
Hesamipour SBalafar MMousazadeh S(2023)Detecting Communities in Complex Networks Using an Adaptive Genetic Algorithm and Node Similarity-Based EncodingComplexity10.1155/2023/47965362023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/4796536
Márquez RWeber R(2023)Dynamic community detection including node attributesExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119791223:COnline publication date: 1-Aug-2023
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Spampinato AScollo RCutello VPavone M(2023)Random search immune algorithm for community detectionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-07999-z27:12(8061-8090)Online publication date: 3-Apr-2023
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