Adaptive fusion of structure and attribute guided polarized communities search

Yang, F; Ma, H; Wang, W; Li, Z; Chang, L

Adaptive fusion of structure and attribute guided polarized communities search

Yang, F Ma, H Wang, W

Li, Z Chang, L

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Publisher:: HIGHER EDUCATION PRESS
Publication Type:: Journal Article
Citation:: Frontiers of Computer Science, 2024, 18, (1)
Issue Date:: 2024-02-01

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Field	Value	Language
dc.contributor.author	Yang, F
dc.contributor.author	Ma, H
dc.contributor.author	Wang, W https://orcid.org/0000-0001-9919-7488
dc.contributor.author	Li, Z
dc.contributor.author	Chang, L
dc.date.accessioned	2025-01-15T03:37:40Z
dc.date.available	2025-01-15T03:37:40Z
dc.date.issued	2024-02-01
dc.identifier.citation	Frontiers of Computer Science, 2024, 18, (1)
dc.identifier.issn	2095-2228
dc.identifier.issn	2095-2236
dc.identifier.uri	http://hdl.handle.net/10453/183566
dc.description.abstract	In this paper, we propose the community search framework searching polarized communities via adaptively fusing structure and attribute in attributed signed networks, which searches for two polarized subgraphs on an attributed signed network for given query nodes. We first conduct a analysis by the similarity of attributes between nodes. And we adaptively integrate topology and node attributes into an augmented signed network. Then, a spectral method based on generalized Rayleigh quotient is proposed. Finally, a linear programming problem is designed to detect polarized communities by local eigenspace. Experiments on real-world datasets demonstrate the effectiveness of our method.
dc.language	English
dc.publisher	HIGHER EDUCATION PRESS
dc.relation.ispartof	Frontiers of Computer Science
dc.relation.isbasedon	10.1007/s11704-023-2776-7
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	46 Information and computing sciences
dc.title	Adaptive fusion of structure and attribute guided polarized communities search
dc.type	Journal Article
utslib.citation.volume	18
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2025-01-15T03:37:39Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	18
utslib.citation.issue	1

Abstract:

In this paper, we propose the community search framework searching polarized communities via adaptively fusing structure and attribute in attributed signed networks, which searches for two polarized subgraphs on an attributed signed network for given query nodes. We first conduct a analysis by the similarity of attributes between nodes. And we adaptively integrate topology and node attributes into an augmented signed network. Then, a spectral method based on generalized Rayleigh quotient is proposed. Finally, a linear programming problem is designed to detect polarized communities by local eigenspace. Experiments on real-world datasets demonstrate the effectiveness of our method.

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http://hdl.handle.net/10453/183566