A particle swarm optimization approach for handling network social balance problem

Qing Cai; Maoguo Gong; Lijia Ma; Shanfeng Wang; Licheng Jiao; Haifeng Du

doi:10.1109/CEC.2015.7257287

A particle swarm optimization approach for handling network social balance problem

Qing Cai
, Maoguo Gong
, Lijia Ma
, Shanfeng Wang
, Licheng Jiao
, Haifeng Du

School of Public Policy and Administration

Xidian University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

Social balance property is an eminent feature of social networks. Many creative efforts concerning social balance have been done. This paper presents an optimization idea to solve the social balance of complex social networks. A single objective optimization model integrating the network balance and the community properties is proposed towards the social balance problem. A discrete particle swarm optimization algorithm is introduced to solve the proposed optimization model. Extensive experiments on synthetic and real-world signed networks demonstrate that the proposed model does make sense and the introduced optimization algorithm is promissing for solving the social balance problem.

Original language	English
Title of host publication	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3186-3191
Number of pages	6
ISBN (Electronic)	9781479974924
DOIs	https://doi.org/10.1109/CEC.2015.7257287
State	Published - 10 Sep 2015
Event	IEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan Duration: 25 May 2015 → 28 May 2015

Publication series

Name	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Conference

Conference	IEEE Congress on Evolutionary Computation, CEC 2015
Country/Territory	Japan
City	Sendai
Period	25/05/15 → 28/05/15

Keywords

community structure
evolutionary computation
particle swarm optimization
signed network
social balance

Access to Document

10.1109/CEC.2015.7257287

Cite this

Cai, Q., Gong, M., Ma, L., Wang, S., Jiao, L., & Du, H. (2015). A particle swarm optimization approach for handling network social balance problem. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 3186-3191). Article 7257287 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2015.7257287

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title = "A particle swarm optimization approach for handling network social balance problem",

abstract = "Social balance property is an eminent feature of social networks. Many creative efforts concerning social balance have been done. This paper presents an optimization idea to solve the social balance of complex social networks. A single objective optimization model integrating the network balance and the community properties is proposed towards the social balance problem. A discrete particle swarm optimization algorithm is introduced to solve the proposed optimization model. Extensive experiments on synthetic and real-world signed networks demonstrate that the proposed model does make sense and the introduced optimization algorithm is promissing for solving the social balance problem.",

keywords = "community structure, evolutionary computation, particle swarm optimization, signed network, social balance",

author = "Qing Cai and Maoguo Gong and Lijia Ma and Shanfeng Wang and Licheng Jiao and Haifeng Du",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Congress on Evolutionary Computation, CEC 2015 ; Conference date: 25-05-2015 Through 28-05-2015",

year = "2015",

month = sep,

day = "10",

doi = "10.1109/CEC.2015.7257287",

language = "英语",

series = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",

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booktitle = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",

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Cai, Q, Gong, M, Ma, L, Wang, S, Jiao, L & Du, H 2015, A particle swarm optimization approach for handling network social balance problem. in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings., 7257287, 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 3186-3191, IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, 25/05/15. https://doi.org/10.1109/CEC.2015.7257287

A particle swarm optimization approach for handling network social balance problem. / Cai, Qing; Gong, Maoguo; Ma, Lijia et al.
2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 3186-3191 7257287 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A particle swarm optimization approach for handling network social balance problem

AU - Cai, Qing

AU - Gong, Maoguo

AU - Ma, Lijia

AU - Wang, Shanfeng

AU - Jiao, Licheng

AU - Du, Haifeng

PY - 2015/9/10

Y1 - 2015/9/10

N2 - Social balance property is an eminent feature of social networks. Many creative efforts concerning social balance have been done. This paper presents an optimization idea to solve the social balance of complex social networks. A single objective optimization model integrating the network balance and the community properties is proposed towards the social balance problem. A discrete particle swarm optimization algorithm is introduced to solve the proposed optimization model. Extensive experiments on synthetic and real-world signed networks demonstrate that the proposed model does make sense and the introduced optimization algorithm is promissing for solving the social balance problem.

AB - Social balance property is an eminent feature of social networks. Many creative efforts concerning social balance have been done. This paper presents an optimization idea to solve the social balance of complex social networks. A single objective optimization model integrating the network balance and the community properties is proposed towards the social balance problem. A discrete particle swarm optimization algorithm is introduced to solve the proposed optimization model. Extensive experiments on synthetic and real-world signed networks demonstrate that the proposed model does make sense and the introduced optimization algorithm is promissing for solving the social balance problem.

KW - community structure

KW - evolutionary computation

KW - particle swarm optimization

KW - signed network

KW - social balance

UR - https://www.scopus.com/pages/publications/84963595166

U2 - 10.1109/CEC.2015.7257287

DO - 10.1109/CEC.2015.7257287

M3 - 会议稿件

AN - SCOPUS:84963595166

T3 - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

SP - 3186

EP - 3191

BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE Congress on Evolutionary Computation, CEC 2015

Y2 - 25 May 2015 through 28 May 2015

ER -

Cai Q, Gong M, Ma L, Wang S, Jiao L, Du H. A particle swarm optimization approach for handling network social balance problem. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 3186-3191. 7257287. (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings). doi: 10.1109/CEC.2015.7257287

A particle swarm optimization approach for handling network social balance problem

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