Towards Digital Twin-Oriented Complex Networked Systems: Introducing heterogeneous node features and interaction rules.

Wen, J; Gabrys, B; Musial, K

Towards Digital Twin-Oriented Complex Networked Systems: Introducing heterogeneous node features and interaction rules.

Wen, J Gabrys, B

Musial, K

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Publisher:: Public Library of Science (PLoS)
Publication Type:: Journal Article
Citation:: PLoS One, 2024, 19, (1), pp. e0296426
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Wen, J
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.contributor.author	Musial, K
dc.contributor.editor	Naldi, M
dc.date.accessioned	2024-01-22T04:29:05Z
dc.date.available	2023-12-11
dc.date.available	2024-01-22T04:29:05Z
dc.date.issued	2024
dc.identifier.citation	PLoS One, 2024, 19, (1), pp. e0296426
dc.identifier.issn	1932-6203
dc.identifier.issn	1932-6203
dc.identifier.uri	http://hdl.handle.net/10453/174836
dc.description.abstract	This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real-world social networked systems. Modelling process focuses on (i) features of nodes and (ii) interaction rules for creating connections that are built based on individual node's preferences. We conduct experiments on simulation-based DT-CNSs that incorporate various features and rules about network growth and different transmissibilities related to an epidemic spread on these networks. We present a case study on disaster resilience of social networks given an epidemic outbreak by investigating the infection occurrence within specific time and social distance. The experimental results show how different levels of the structural and dynamics complexities, concerned with feature diversity and flexibility of interaction rules respectively, influence network growth and epidemic spread. The analysis revealed that, to achieve maximum disaster resilience, mitigation policies should be targeted at nodes with preferred features as they have higher infection risks and should be the focus of the epidemic control.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Public Library of Science (PLoS)
dc.relation	http://purl.org/au-research/grants/arc/DP190101087
dc.relation.ispartof	PLoS One
dc.relation.isbasedon	10.1371/journal.pone.0296426
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	General Science & Technology
dc.subject.mesh	Humans
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Disasters
dc.subject.mesh	Epidemics
dc.subject.mesh	Disease Susceptibility
dc.subject.mesh	Humans
dc.subject.mesh	Disease Susceptibility
dc.subject.mesh	Disasters
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Epidemics
dc.subject.mesh	Humans
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Disasters
dc.subject.mesh	Epidemics
dc.subject.mesh	Disease Susceptibility
dc.title	Towards Digital Twin-Oriented Complex Networked Systems: Introducing heterogeneous node features and interaction rules.
dc.type	Journal Article
utslib.citation.volume	19
utslib.location.activity	United States
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/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2024-01-22T04:28:58Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	19
utslib.citation.issue	1

Abstract:

This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real-world social networked systems. Modelling process focuses on (i) features of nodes and (ii) interaction rules for creating connections that are built based on individual node's preferences. We conduct experiments on simulation-based DT-CNSs that incorporate various features and rules about network growth and different transmissibilities related to an epidemic spread on these networks. We present a case study on disaster resilience of social networks given an epidemic outbreak by investigating the infection occurrence within specific time and social distance. The experimental results show how different levels of the structural and dynamics complexities, concerned with feature diversity and flexibility of interaction rules respectively, influence network growth and epidemic spread. The analysis revealed that, to achieve maximum disaster resilience, mitigation policies should be targeted at nodes with preferred features as they have higher infection risks and should be the focus of the epidemic control.

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http://hdl.handle.net/10453/174836