An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks

Community structure, one of the most popular properties in complex networks, has long been a cornerstone in the advance of various scientific branches. Over the past few years, a number of tools have been used in the development of community detection algorithms. In this paper, by means of fusing un...

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Main Authors: Feifan Wang, Baihai Zhang, Senchun Chai, Yuanqing Xia
Format: Article
Language:English
Published: Wiley 2018-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2018/8098325
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author Feifan Wang
Baihai Zhang
Senchun Chai
Yuanqing Xia
author_facet Feifan Wang
Baihai Zhang
Senchun Chai
Yuanqing Xia
author_sort Feifan Wang
collection DOAJ
description Community structure, one of the most popular properties in complex networks, has long been a cornerstone in the advance of various scientific branches. Over the past few years, a number of tools have been used in the development of community detection algorithms. In this paper, by means of fusing unsupervised extreme learning machines and the k-means clustering techniques, we propose a novel community detection method that surpasses traditional k-means approaches in terms of precision and stability while adding very few extra computational costs. Furthermore, results of extensive experiments undertaken on computer-generated networks and real-world datasets illustrate acceptable performances of the introduced algorithm in comparison with other typical community detection algorithms.
format Article
id doaj-art-b95e12d981ea475795658ed5ad93e4f7
institution Kabale University
issn 1076-2787
1099-0526
language English
publishDate 2018-01-01
publisher Wiley
record_format Article
series Complexity
spelling doaj-art-b95e12d981ea475795658ed5ad93e4f72025-02-03T05:48:29ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/80983258098325An Extreme Learning Machine-Based Community Detection Algorithm in Complex NetworksFeifan Wang0Baihai Zhang1Senchun Chai2Yuanqing Xia3School of Automation, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Beijing 100081, ChinaSchool of Automation, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Beijing 100081, ChinaSchool of Automation, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Beijing 100081, ChinaSchool of Automation, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Beijing 100081, ChinaCommunity structure, one of the most popular properties in complex networks, has long been a cornerstone in the advance of various scientific branches. Over the past few years, a number of tools have been used in the development of community detection algorithms. In this paper, by means of fusing unsupervised extreme learning machines and the k-means clustering techniques, we propose a novel community detection method that surpasses traditional k-means approaches in terms of precision and stability while adding very few extra computational costs. Furthermore, results of extensive experiments undertaken on computer-generated networks and real-world datasets illustrate acceptable performances of the introduced algorithm in comparison with other typical community detection algorithms.http://dx.doi.org/10.1155/2018/8098325
spellingShingle Feifan Wang
Baihai Zhang
Senchun Chai
Yuanqing Xia
An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks
Complexity
title An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks
title_full An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks
title_fullStr An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks
title_full_unstemmed An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks
title_short An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks
title_sort extreme learning machine based community detection algorithm in complex networks
url http://dx.doi.org/10.1155/2018/8098325
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