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  Efficient Link-Based Spatial Network Disintegration Strategy

Wang, Z., Deng, Y., Wang, Z., Kurths, J., Wu, J. (2025): Efficient Link-Based Spatial Network Disintegration Strategy. - IEEE Transactions on Network Science and Engineering, 12, 2, 1096-1111.
https://doi.org/10.1109/TNSE.2024.3523952

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Wang, Zhigang1, Autor              
Deng, Ye2, Autor
Wang, Ze2, Autor
Kurths, Jürgen1, Autor              
Wu, Jun2, Autor
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Zusammenfassung: Many real complex systems, such as infrastructure and the Internet, are not random but embedded in a metric space. The problem of spatial network disintegration, or critical area identification, is a fundamental research domain in network science and has received increasing attention. Typical applications include network immunization, epidemic control, and early warning signals of disintegration. Due to the computationally challenging (NP-hard) problem, they usually cannot be solved with polynomial algorithms. Here, we propose an efficient disintegration method in spatial networks through a link-based strategy. First, we introduce a regional failure model with multiple disintegration circles for the spatial network. We then calculate the sum of the specific attribute values of the links in the circle to identify the critical regions of the spatial network, which also correspond to the geographic regions where disintegration occurs. Extensive experiments on real-world networks of different types demonstrate that the strategy outperforms conventional methods in terms of solution quality.

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Sprache(n): eng - Englisch
 Datum: 2025-01-062025-03-01
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1109/TNSE.2024.3523952
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Model / method: Machine Learning
 Art des Abschluß: -

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Titel: IEEE Transactions on Network Science and Engineering
Genre der Quelle: Zeitschrift, SCI, Scopus
 Urheber:
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 12 (2) Artikelnummer: - Start- / Endseite: 1096 - 1111 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-network-sience-engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)