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  Spatial network disintegration based on spatial coverage

Deng, Y., Wang, Z., Xiao, Y., Shen, X., Kurths, J., Wu, J. (2024 online): Spatial network disintegration based on spatial coverage. - Reliability Engineering & System Safety, 253, 110525.
https://doi.org/10.1016/j.ress.2024.110525

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 Urheber:
Deng, Ye1, Autor
Wang, Zhigang1, Autor
Xiao, Yu1, Autor
Shen, Xiaoda1, Autor
Kurths, Jürgen2, Autor              
Wu, Jun1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: The problem of network disintegration, such as interrupting rumor spreading networks and dismantling terrorist networks, involves evaluating changes in network performance. However, traditional metrics primarily focus on the topological structure and often neglect the crucial spatial attributes of nodes and edges, thereby failing to capture the spatial functional losses. Here we first introduce the concept of spatial coverage to evaluate the spatial network performance, which is defined as the convex hull area of the largest connected component. Then a greedy algorithm is proposed to maximize the reduction of the convex hull area through strategic node removals. Extensive experiments verified that the spatial coverage metric can effectively quantify changes in the performance of spatial networks, and the proposed algorithm can maximize the reduction of the convex hull area of the largest connected component compared to genetic algorithm and centrality strategies. Specifically, our algorithm reduces the convex hull area by up to 30% compared to the best-performing strategy. These results indicate that the critical nodes influencing network performance are a combination of numerous peripheral spatial leaf nodes and a few central spatial core nodes. This study substantially enhances our understanding of spatial network robustness and provides a novel perspective for network optimization.

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Sprache(n): eng - Englisch
 Datum: 2024-10-16
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.ress.2024.110525
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Complex Networks
Model / method: Machine Learning
 Art des Abschluß: -

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Titel: Reliability Engineering & System Safety
Genre der Quelle: Zeitschrift, SCI, Scopus
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 253 Artikelnummer: 110525 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1879-0836
Publisher: Elsevier