Applicability of the minimal dominating set for influence maximization in multilayer networks

Czuba, M; Jia, M; Brodka, P; Musial, K

Applicability of the minimal dominating set for influence maximization in multilayer networks

Czuba, M Jia, M

Brodka, P

Musial, K

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Publisher:: OXFORD UNIV PRESS
Publication Type:: Journal Article
Citation:: JOURNAL OF COMPLEX NETWORKS, 2025, 13, (6)
Issue Date:: 2025-12

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Field	Value	Language
dc.contributor.author	Czuba, M
dc.contributor.author	Jia, M https://orcid.org/0000-0001-8378-3407
dc.contributor.author	Brodka, P https://orcid.org/0000-0002-6474-0089
dc.contributor.author	Musial, K
dc.date.accessioned	2026-01-05T03:54:07Z
dc.date.available	2026-01-05T03:54:07Z
dc.date.issued	2025-12
dc.identifier.citation	JOURNAL OF COMPLEX NETWORKS, 2025, 13, (6)
dc.identifier.issn	2051-1310
dc.identifier.issn	2051-1329
dc.identifier.uri	http://hdl.handle.net/10453/191213
dc.description.abstract	Abstract The minimal dominating set (MDS) is a well-established concept in network controllability and has been successfully applied in various domains, including sensor placement, network resilience, and epidemic containment. In this study, we adapt the local-improvement MDS routine and explore its potential for enhancing seed selection for influence maximization in multilayer networks. We employ the Linear Threshold Model, which provides an intuitive representation of influence spread or opinion dynamics by accounting for the accumulation of peer influence. To ensure interpretability, we utilize rank-refining seed selection methods, with the results further filtered with MDS. Our findings reveal that incorporating MDS into the seed selection process improves spread only within a specific range of situations. Notably, the improvement is observed for larger seed set budgets, lower activation thresholds, and when an $ AND $ strategy is used to aggregate influence across network layers. This scenario reflects situations where an individual does not require the majority of their acquaintances to hold a target opinion, but must be influenced across all social circles.
dc.language	English
dc.publisher	OXFORD UNIV PRESS
dc.relation	http://purl.org/au-research/grants/arc/DP190101087
dc.relation.ispartof	JOURNAL OF COMPLEX NETWORKS
dc.relation.isbasedon	10.1093/comnet/cnaf036
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0101 Pure Mathematics, 0102 Applied Mathematics, 0103 Numerical and Computational Mathematics
dc.subject.classification	4901 Applied mathematics
dc.title	Applicability of the minimal dominating set for influence maximization in multilayer networks
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	0101 Pure Mathematics
utslib.for	0102 Applied Mathematics
utslib.for	0103 Numerical and Computational Mathematics
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
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-01-05T03:54:06Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	6

Abstract:

Abstract The minimal dominating set (MDS) is a well-established concept in network controllability and has been successfully applied in various domains, including sensor placement, network resilience, and epidemic containment. In this study, we adapt the local-improvement MDS routine and explore its potential for enhancing seed selection for influence maximization in multilayer networks. We employ the Linear Threshold Model, which provides an intuitive representation of influence spread or opinion dynamics by accounting for the accumulation of peer influence. To ensure interpretability, we utilize rank-refining seed selection methods, with the results further filtered with MDS. Our findings reveal that incorporating MDS into the seed selection process improves spread only within a specific range of situations. Notably, the improvement is observed for larger seed set budgets, lower activation thresholds, and when an $ AND $ strategy is used to aggregate influence across network layers. This scenario reflects situations where an individual does not require the majority of their acquaintances to hold a target opinion, but must be influenced across all social circles.

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http://hdl.handle.net/10453/191213