Effective Semisupervised Community Detection Using Negative Information

Effective Semisupervised Community Detection Using Negative Information

The semisupervised community detection method, which can utilize prior information to guide the discovery process of community structure, has aroused considerable research interests in the past few years. Most of the former works assume that the exact labels of some nodes are known in advance and pr...

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Main Authors: Dong Liu, Dequan Duan, Shikai Sui, Guojie Song
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2015/109671
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spelling doaj-ecf65924712449188dfc01fc603672382020-11-24T22:24:08ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/109671109671Effective Semisupervised Community Detection Using Negative InformationDong Liu0Dequan Duan1Shikai Sui2Guojie Song3School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaSchool of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaSchool of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaSchool of Electronics Engineering and Computer Science, Peking University, Beijing 100871, ChinaThe semisupervised community detection method, which can utilize prior information to guide the discovery process of community structure, has aroused considerable research interests in the past few years. Most of the former works assume that the exact labels of some nodes are known in advance and presented in the forms of individual labels and pairwise constraints. In this paper, we propose a novel type of prior information called negative information, which indicates whether a node does not belong to a specific community. Then the semisupervised community detection algorithm is presented based on negative information to efficiently make use of this type of information to assist the process of community detection. The proposed algorithm is evaluated on several artificial and real-world networks and shows high effectiveness in recovering communities.http://dx.doi.org/10.1155/2015/109671
collection DOAJ
language English
format Article
sources DOAJ
author Dong Liu
Dequan Duan
Shikai Sui
Guojie Song
spellingShingle Dong Liu
Dequan Duan
Shikai Sui
Guojie Song
Effective Semisupervised Community Detection Using Negative Information
Mathematical Problems in Engineering
author_facet Dong Liu
Dequan Duan
Shikai Sui
Guojie Song
author_sort Dong Liu
title Effective Semisupervised Community Detection Using Negative Information
title_short Effective Semisupervised Community Detection Using Negative Information
title_full Effective Semisupervised Community Detection Using Negative Information
title_fullStr Effective Semisupervised Community Detection Using Negative Information
title_full_unstemmed Effective Semisupervised Community Detection Using Negative Information
title_sort effective semisupervised community detection using negative information
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2015-01-01
description The semisupervised community detection method, which can utilize prior information to guide the discovery process of community structure, has aroused considerable research interests in the past few years. Most of the former works assume that the exact labels of some nodes are known in advance and presented in the forms of individual labels and pairwise constraints. In this paper, we propose a novel type of prior information called negative information, which indicates whether a node does not belong to a specific community. Then the semisupervised community detection algorithm is presented based on negative information to efficiently make use of this type of information to assist the process of community detection. The proposed algorithm is evaluated on several artificial and real-world networks and shows high effectiveness in recovering communities.
url http://dx.doi.org/10.1155/2015/109671
work_keys_str_mv AT dongliu effectivesemisupervisedcommunitydetectionusingnegativeinformation
AT dequanduan effectivesemisupervisedcommunitydetectionusingnegativeinformation
AT shikaisui effectivesemisupervisedcommunitydetectionusingnegativeinformation
AT guojiesong effectivesemisupervisedcommunitydetectionusingnegativeinformation
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