Global Reinforcement of Social Networks: The Anchored Coreness Problem

Linghu, Q; Zhang, F; Lin, X; Zhang, W; Zhang, Y

Global Reinforcement of Social Networks: The Anchored Coreness Problem

Linghu, Q Zhang, F Lin, X Zhang, W Zhang, Y

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: Proceedings of the ACM SIGMOD International Conference on Management of Data, 2020, pp. 2211-2226
Issue Date:: 2020-06-14

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Field	Value	Language
dc.contributor.author	Linghu, Q
dc.contributor.author	Zhang, F
dc.contributor.author	Lin, X
dc.contributor.author	Zhang, W
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638
dc.date.accessioned	2020-07-15T06:30:35Z
dc.date.available	2020-07-15T06:30:35Z
dc.date.issued	2020-06-14
dc.identifier.citation	Proceedings of the ACM SIGMOD International Conference on Management of Data, 2020, pp. 2211-2226
dc.identifier.isbn	9781450367356
dc.identifier.issn	0730-8078
dc.identifier.uri	http://hdl.handle.net/10453/141781
dc.description.abstract	© 2020 Association for Computing Machinery. The stability of a social network has been widely studied as an important indicator for both the network holders and the participants. Existing works on reinforcing networks focus on a local view, e.g., the anchored k-core problem aims to enlarge the size of the k-core with a fixed input k. Nevertheless, it is more promising to reinforce a social network in a global manner: considering the engagement of every user (vertex) in the network. Since the coreness of a user has been validated as the "best practice" for capturing user engagement, we propose and study the anchored coreness problem in this paper: anchoring a small number of vertices to maximize the coreness gain (the total increment of coreness) of all the vertices in the network. We prove the problem is NP-hard and show it is more challenging than the existing local-view problems. An efficient heuristic algorithm is proposed with novel techniques on pruning search space and reusing the intermediate results. Extensive experiments on real-life data demonstrate that our model is effective for reinforcing social networks and our algorithm is efficient.
dc.language	en
dc.publisher	ACM
dc.relation	http://purl.org/au-research/grants/arc/FT170100128
dc.relation	http://purl.org/au-research/grants/arc/DP180103096
dc.relation.ispartof	Proceedings of the ACM SIGMOD International Conference on Management of Data
dc.relation.ispartof	SIGMOD/PODS '20: International Conference on Management of Data
dc.relation.isbasedon	10.1145/3318464.3389744
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Global Reinforcement of Social Networks: The Anchored Coreness Problem
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-07-15T06:30:30Z
pubs.publication-status	Published

Abstract:

© 2020 Association for Computing Machinery. The stability of a social network has been widely studied as an important indicator for both the network holders and the participants. Existing works on reinforcing networks focus on a local view, e.g., the anchored k-core problem aims to enlarge the size of the k-core with a fixed input k. Nevertheless, it is more promising to reinforce a social network in a global manner: considering the engagement of every user (vertex) in the network. Since the coreness of a user has been validated as the "best practice" for capturing user engagement, we propose and study the anchored coreness problem in this paper: anchoring a small number of vertices to maximize the coreness gain (the total increment of coreness) of all the vertices in the network. We prove the problem is NP-hard and show it is more challenging than the existing local-view problems. An efficient heuristic algorithm is proposed with novel techniques on pruning search space and reusing the intermediate results. Extensive experiments on real-life data demonstrate that our model is effective for reinforcing social networks and our algorithm is efficient.

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http://hdl.handle.net/10453/141781