A multimodal optimization and surprise based consensus community detection algorithm

Guanbo Jia; Shan He; Zexuan Zhu; Ke Tang; Jing Liu

doi:10.1145/2739482.2764656

A multimodal optimization and surprise based consensus community detection algorithm

Guanbo Jia, Shan He^*, Zexuan Zhu, Ke Tang, Jing Liu

^*Corresponding author for this work

Computer Science

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

Abstract

A new community structure measure called Surprise has been proposed to address the resolution limit problem of modularity. However, our analysis shows that, similar to modularity, Surprise also suffers from the so-called extreme degeneracy problem, which leads to unstable module identification results. To solve this problem, we propose a novel Multimodal Optimization and Surprise based Consensus Community Detection (MOSCCoD) algorithm. Experimental results show that MOSCCoD has overcome the extreme degeneracy problem of Surprise and shown a very competitive performance in terms of stability and accuracy.

Original language	English
Title of host publication	GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference
Editors	Sara Silva
Publisher	Association for Computing Machinery
Pages	1407-1408
Number of pages	2
ISBN (Electronic)	9781450334884
DOIs	https://doi.org/10.1145/2739482.2764656
Publication status	Published - 11 Jul 2015
Event	17th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain Duration: 11 Jul 2015 → 15 Jul 2015

Publication series

Name	GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference

Conference

Conference	17th Genetic and Evolutionary Computation Conference, GECCO 2015
Country/Territory	Spain
City	Madrid
Period	11/07/15 → 15/07/15

Bibliographical note

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

Community detection
Complex network
Consensus clustering
Extreme degeneracy
Multimodal optimization

ASJC Scopus subject areas

Software
Theoretical Computer Science
Artificial Intelligence

Access to Document

10.1145/2739482.2764656

Cite this

Jia, G., He, S., Zhu, Z., Tang, K., & Liu, J. (2015). A multimodal optimization and surprise based consensus community detection algorithm. In S. Silva (Ed.), GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference (pp. 1407-1408). (GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference). Association for Computing Machinery . https://doi.org/10.1145/2739482.2764656

Jia, Guanbo ; He, Shan ; Zhu, Zexuan et al. / A multimodal optimization and surprise based consensus community detection algorithm. GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. editor / Sara Silva. Association for Computing Machinery , 2015. pp. 1407-1408 (GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference).

@inproceedings{9c999cad12c041d2bc88aefafb33910c,

title = "A multimodal optimization and surprise based consensus community detection algorithm",

abstract = "A new community structure measure called Surprise has been proposed to address the resolution limit problem of modularity. However, our analysis shows that, similar to modularity, Surprise also suffers from the so-called extreme degeneracy problem, which leads to unstable module identification results. To solve this problem, we propose a novel Multimodal Optimization and Surprise based Consensus Community Detection (MOSCCoD) algorithm. Experimental results show that MOSCCoD has overcome the extreme degeneracy problem of Surprise and shown a very competitive performance in terms of stability and accuracy.",

keywords = "Community detection, Complex network, Consensus clustering, Extreme degeneracy, Multimodal optimization",

author = "Guanbo Jia and Shan He and Zexuan Zhu and Ke Tang and Jing Liu",

year = "2015",

month = jul,

day = "11",

doi = "10.1145/2739482.2764656",

language = "English",

series = "GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference",

publisher = "Association for Computing Machinery ",

pages = "1407--1408",

editor = "Sara Silva",

booktitle = "GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference",

}

Jia, G, He, S, Zhu, Z, Tang, K & Liu, J 2015, A multimodal optimization and surprise based consensus community detection algorithm. in S Silva (ed.), GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, Association for Computing Machinery , pp. 1407-1408, 17th Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, 11/07/15. https://doi.org/10.1145/2739482.2764656

A multimodal optimization and surprise based consensus community detection algorithm. / Jia, Guanbo; He, Shan; Zhu, Zexuan et al.
GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. ed. / Sara Silva. Association for Computing Machinery , 2015. p. 1407-1408 (GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference).

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

TY - GEN

T1 - A multimodal optimization and surprise based consensus community detection algorithm

AU - Jia, Guanbo

AU - He, Shan

AU - Zhu, Zexuan

AU - Tang, Ke

AU - Liu, Jing

PY - 2015/7/11

Y1 - 2015/7/11

N2 - A new community structure measure called Surprise has been proposed to address the resolution limit problem of modularity. However, our analysis shows that, similar to modularity, Surprise also suffers from the so-called extreme degeneracy problem, which leads to unstable module identification results. To solve this problem, we propose a novel Multimodal Optimization and Surprise based Consensus Community Detection (MOSCCoD) algorithm. Experimental results show that MOSCCoD has overcome the extreme degeneracy problem of Surprise and shown a very competitive performance in terms of stability and accuracy.

AB - A new community structure measure called Surprise has been proposed to address the resolution limit problem of modularity. However, our analysis shows that, similar to modularity, Surprise also suffers from the so-called extreme degeneracy problem, which leads to unstable module identification results. To solve this problem, we propose a novel Multimodal Optimization and Surprise based Consensus Community Detection (MOSCCoD) algorithm. Experimental results show that MOSCCoD has overcome the extreme degeneracy problem of Surprise and shown a very competitive performance in terms of stability and accuracy.

KW - Community detection

KW - Complex network

KW - Consensus clustering

KW - Extreme degeneracy

KW - Multimodal optimization

UR - http://www.scopus.com/inward/record.url?scp=84959386656&partnerID=8YFLogxK

U2 - 10.1145/2739482.2764656

DO - 10.1145/2739482.2764656

M3 - Conference contribution

AN - SCOPUS:84959386656

T3 - GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference

SP - 1407

EP - 1408

BT - GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference

A2 - Silva, Sara

PB - Association for Computing Machinery

T2 - 17th Genetic and Evolutionary Computation Conference, GECCO 2015

Y2 - 11 July 2015 through 15 July 2015

ER -

Jia G, He S, Zhu Z, Tang K, Liu J. A multimodal optimization and surprise based consensus community detection algorithm. In Silva S, editor, GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. Association for Computing Machinery . 2015. p. 1407-1408. (GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference). doi: 10.1145/2739482.2764656

A multimodal optimization and surprise based consensus community detection algorithm

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this