A Compression-Based Multi-Objective Evolutionary Algorithm for Community Detection in Social Networks
Community detection is a key aspect for understanding network structures and uncovers the underlying functions or characteristics of complex systems. A community usually refers to a set of nodes that are densely connected among themselves, but sparsely connected to the remaining nodes of the network...
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doaj-102c93ebbb0f4808b3532109f00a76242021-03-30T03:07:35ZengIEEEIEEE Access2169-35362020-01-018621376215010.1109/ACCESS.2020.29846389051693A Compression-Based Multi-Objective Evolutionary Algorithm for Community Detection in Social NetworksZhiyuan Liu0https://orcid.org/0000-0002-0847-5962Yinghong Ma1Xiujuan Wang2https://orcid.org/0000-0002-6163-2314Business School, Shandong Normal University, Jinan, ChinaBusiness School, Shandong Normal University, Jinan, ChinaSchool of Business, Dalian University of Technology, Dalian, ChinaCommunity detection is a key aspect for understanding network structures and uncovers the underlying functions or characteristics of complex systems. A community usually refers to a set of nodes that are densely connected among themselves, but sparsely connected to the remaining nodes of the network. Detecting communities has been proved to be a NP-hard problem. Therefore, evolutionary based optimization approaches can be used to solve it. But a primary challenge for them is the higher computational complexity when dealing with large scale networks. In this respect, a COMpression based Multi-Objective Evolutionary Algorithm with Decomposition (Com-MOEA/D) for community detection is proposed where the network is first compressed to a much more smaller scale by exploring network topologies. After that, a framework of multi-objective evolutionary algorithm based on decomposition is applied, in which a local information based genetic operator is proposed to speed up the convergence and improve the accuracy of the Com-MOEA/D algorithm. Experimental results on both real world and synthetic networks show the superiority of the proposed method over several state-of-the-art community detection algorithms.https://ieeexplore.ieee.org/document/9051693/Network compressionmulti-objective optimizationcommunity detectionsocial networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhiyuan Liu Yinghong Ma Xiujuan Wang |
spellingShingle |
Zhiyuan Liu Yinghong Ma Xiujuan Wang A Compression-Based Multi-Objective Evolutionary Algorithm for Community Detection in Social Networks IEEE Access Network compression multi-objective optimization community detection social networks |
author_facet |
Zhiyuan Liu Yinghong Ma Xiujuan Wang |
author_sort |
Zhiyuan Liu |
title |
A Compression-Based Multi-Objective Evolutionary Algorithm for Community Detection in Social Networks |
title_short |
A Compression-Based Multi-Objective Evolutionary Algorithm for Community Detection in Social Networks |
title_full |
A Compression-Based Multi-Objective Evolutionary Algorithm for Community Detection in Social Networks |
title_fullStr |
A Compression-Based Multi-Objective Evolutionary Algorithm for Community Detection in Social Networks |
title_full_unstemmed |
A Compression-Based Multi-Objective Evolutionary Algorithm for Community Detection in Social Networks |
title_sort |
compression-based multi-objective evolutionary algorithm for community detection in social networks |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Community detection is a key aspect for understanding network structures and uncovers the underlying functions or characteristics of complex systems. A community usually refers to a set of nodes that are densely connected among themselves, but sparsely connected to the remaining nodes of the network. Detecting communities has been proved to be a NP-hard problem. Therefore, evolutionary based optimization approaches can be used to solve it. But a primary challenge for them is the higher computational complexity when dealing with large scale networks. In this respect, a COMpression based Multi-Objective Evolutionary Algorithm with Decomposition (Com-MOEA/D) for community detection is proposed where the network is first compressed to a much more smaller scale by exploring network topologies. After that, a framework of multi-objective evolutionary algorithm based on decomposition is applied, in which a local information based genetic operator is proposed to speed up the convergence and improve the accuracy of the Com-MOEA/D algorithm. Experimental results on both real world and synthetic networks show the superiority of the proposed method over several state-of-the-art community detection algorithms. |
topic |
Network compression multi-objective optimization community detection social networks |
url |
https://ieeexplore.ieee.org/document/9051693/ |
work_keys_str_mv |
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