Robustness of community networks against cascading failures with heterogeneous redistribution strategies

Song, B; Wu, HM; Song, YR; Jiang, GP; Xia, LL; Wang, X

Robustness of community networks against cascading failures with heterogeneous redistribution strategies

Song, B Wu, HM Song, YR Jiang, GP Xia, LL Wang, X

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Publisher:: IOP Publishing
Publication Type:: Journal Article
Citation:: Chinese Physics B, 2023, 32, (9), pp. 098905
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Song, B
dc.contributor.author	Wu, HM
dc.contributor.author	Song, YR
dc.contributor.author	Jiang, GP
dc.contributor.author	Xia, LL
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9439-6437
dc.date.accessioned	2024-04-22T11:45:42Z
dc.date.available	2024-04-22T11:45:42Z
dc.date.issued	2023-09-01
dc.identifier.citation	Chinese Physics B, 2023, 32, (9), pp. 098905
dc.identifier.issn	1674-1056
dc.identifier.issn	2058-3834
dc.identifier.uri	http://hdl.handle.net/10453/178208
dc.description.abstract	Network robustness is one of the core contents of complex network security research. This paper focuses on the robustness of community networks with respect to cascading failures, considering the nodes influence and community heterogeneity. A novel node influence ranking method, community-based Clustering-LeaderRank (CCL) algorithm, is first proposed to identify influential nodes in community networks. Simulation results show that the CCL method can effectively identify the influence of nodes. Based on node influence, a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks. Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process. The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities. When the initial load distribution and the load redistribution strategy based on the node influence are the same, the network shows better robustness against node failure.
dc.language	en
dc.publisher	IOP Publishing
dc.relation.ispartof	Chinese Physics B
dc.relation.isbasedon	10.1088/1674-1056/acd9c3
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 02 Physical Sciences, 09 Engineering
dc.subject.classification	General Physics
dc.subject.classification	40 Engineering
dc.subject.classification	49 Mathematical sciences
dc.subject.classification	51 Physical sciences
dc.title	Robustness of community networks against cascading failures with heterogeneous redistribution strategies
dc.type	Journal Article
utslib.citation.volume	32
utslib.for	01 Mathematical Sciences
utslib.for	02 Physical Sciences
utslib.for	09 Engineering
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 Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-22T11:45:41Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	32
utslib.citation.issue	9

Abstract:

Network robustness is one of the core contents of complex network security research. This paper focuses on the robustness of community networks with respect to cascading failures, considering the nodes influence and community heterogeneity. A novel node influence ranking method, community-based Clustering-LeaderRank (CCL) algorithm, is first proposed to identify influential nodes in community networks. Simulation results show that the CCL method can effectively identify the influence of nodes. Based on node influence, a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks. Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process. The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities. When the initial load distribution and the load redistribution strategy based on the node influence are the same, the network shows better robustness against node failure.

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http://hdl.handle.net/10453/178208