Community detection in complex network based on APT method

Chen, Q; Qiao, YL; Hu, F; Li, Y; Tan, K; Zhu, M; Zhang, C

Community detection in complex network based on APT method

Chen, Q Qiao, YL Hu, F Li, Y Tan, K Zhu, M Zhang, C

Permalink

Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Pattern Recognition Letters, 2020, 138, pp. 193-200
Issue Date:: 2020-10-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0167865520302671-main.pdf	Published version	1.28 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Chen, Q
dc.contributor.author	Qiao, YL
dc.contributor.author	Hu, F
dc.contributor.author	Li, Y
dc.contributor.author	Tan, K
dc.contributor.author	Zhu, M
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154
dc.date.accessioned	2020-09-21T04:10:17Z
dc.date.available	2020-09-21T04:10:17Z
dc.date.issued	2020-10-01
dc.identifier.citation	Pattern Recognition Letters, 2020, 138, pp. 193-200
dc.identifier.issn	0167-8655
dc.identifier.uri	http://hdl.handle.net/10453/142788
dc.description.abstract	© 2020 Elsevier B.V. Community detection is a significant methodology in network science. Traditional methods show limitations in dealing with multi-scale and high-dimensional complex data. As one of the most popular unsupervised algorithms, affinity propagation algorithm (AP) has been widely applied in community detection. However, its negative Euclidean similarity and inflexible parameter may lead to high time or memory consumption and excessive detection. Thus, this article presents a novel affinity propagation algorithm in t-distribution (APT), integrated with manifold learning, for detecting community structure. In APT algorithm, the data is compressed by dimensionality reduction, and joint probability is applied to construct the similarity matrix. Further, based on optimized modularity, parameters are adjusted to improve the accuracy. Experiments show that APT has better adaptability and universality than AP algorithm. In contrast to other mainstream algorithms, our algorithm can extract more meaningful communities from multi-scale and high-dimensional networks.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Pattern Recognition Letters
dc.relation.isbasedon	10.1016/j.patrec.2020.07.021
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Community detection in complex network based on APT method
dc.type	Journal Article
utslib.citation.volume	138
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-09-21T04:10:13Z
pubs.publication-status	Accepted
pubs.volume	138

Abstract:

© 2020 Elsevier B.V. Community detection is a significant methodology in network science. Traditional methods show limitations in dealing with multi-scale and high-dimensional complex data. As one of the most popular unsupervised algorithms, affinity propagation algorithm (AP) has been widely applied in community detection. However, its negative Euclidean similarity and inflexible parameter may lead to high time or memory consumption and excessive detection. Thus, this article presents a novel affinity propagation algorithm in t-distribution (APT), integrated with manifold learning, for detecting community structure. In APT algorithm, the data is compressed by dimensionality reduction, and joint probability is applied to construct the similarity matrix. Further, based on optimized modularity, parameters are adjusted to improve the accuracy. Experiments show that APT has better adaptability and universality than AP algorithm. In contrast to other mainstream algorithms, our algorithm can extract more meaningful communities from multi-scale and high-dimensional networks.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/142788