Learning Distilled Graph for Large-Scale Social Network Data Clustering

Liu, W; Gong, D; Tan, M; Shi, JQ; Yang, Y; Hauptmann, AG

Learning Distilled Graph for Large-Scale Social Network Data Clustering

Liu, W Gong, D Tan, M Shi, JQ Yang, Y

Hauptmann, AG

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2020, 32, (7), pp. 1393-1404
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Liu, W
dc.contributor.author	Gong, D
dc.contributor.author	Tan, M
dc.contributor.author	Shi, JQ
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546
dc.contributor.author	Hauptmann, AG
dc.date.accessioned	2021-02-25T19:59:48Z
dc.date.available	2021-02-25T19:59:48Z
dc.date.issued	2020-07-01
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2020, 32, (7), pp. 1393-1404
dc.identifier.issn	1041-4347
dc.identifier.issn	1558-2191
dc.identifier.uri	http://hdl.handle.net/10453/146471
dc.description.abstract	© 1989-2012 IEEE. Spectral analysis is critical in social network analysis. As a vital step of the spectral analysis, the graph construction in many existing works utilizes content data only. Unfortunately, the content data often consists of noisy, sparse, and redundant features, which makes the resulting graph unstable and unreliable. In practice, besides the content data, social network data also contain link information, which provides additional information for graph construction. Some of previous works utilize the link data. However, the link data is often incomplete, which makes the resulting graph incomplete. To address these issues, we propose a novel Distilled Graph Clustering (DGC) method. It pursuits a distilled graph based on both the content data and the link data. The proposed algorithm alternates between two steps: in the feature selection step, it finds the most representative feature subset w.r.t. an intermediate graph initialized with link data; in graph distillation step, the proposed method updates and refines the graph based on only the selected features. The final resulting graph, which is referred to as the distilled graph, is then utilized for spectral clustering on the large-scale social network data. Extensive experiments demonstrate the superiority of the proposed method.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering
dc.relation.isbasedon	10.1109/TKDE.2019.2904068
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.title	Learning Distilled Graph for Large-Scale Social Network Data Clustering
dc.type	Journal Article
utslib.citation.volume	32
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-25T19:59:46Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	32
utslib.citation.issue	7

Abstract:

© 1989-2012 IEEE. Spectral analysis is critical in social network analysis. As a vital step of the spectral analysis, the graph construction in many existing works utilizes content data only. Unfortunately, the content data often consists of noisy, sparse, and redundant features, which makes the resulting graph unstable and unreliable. In practice, besides the content data, social network data also contain link information, which provides additional information for graph construction. Some of previous works utilize the link data. However, the link data is often incomplete, which makes the resulting graph incomplete. To address these issues, we propose a novel Distilled Graph Clustering (DGC) method. It pursuits a distilled graph based on both the content data and the link data. The proposed algorithm alternates between two steps: in the feature selection step, it finds the most representative feature subset w.r.t. an intermediate graph initialized with link data; in graph distillation step, the proposed method updates and refines the graph based on only the selected features. The final resulting graph, which is referred to as the distilled graph, is then utilized for spectral clustering on the large-scale social network data. Extensive experiments demonstrate the superiority of the proposed method.

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http://hdl.handle.net/10453/146471