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  Spatial network disintegration based on spatial coverage

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

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資料種別: 学術論文

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deng_2024_1-s2.0-S0951832024005970-main.pdf (出版社版), 4MB
 
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deng_2024_1-s2.0-S0951832024005970-main.pdf
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作成者

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 作成者:
Deng, Ye1, 著者
Wang, Zhigang1, 著者
Xiao, Yu1, 著者
Shen, Xiaoda1, 著者
Kurths, Jürgen2, 著者              
Wu, Jun1, 著者
所属:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 要旨: 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.

資料詳細

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言語: eng - 英語
 日付: 2024-10-16
 出版の状態: オンラインで出版済み
 ページ: -
 出版情報: -
 目次: -
 査読: 査読あり
 識別子(DOI, ISBNなど): 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
 学位: -

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出版物 1

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出版物名: Reliability Engineering & System Safety
種別: 学術雑誌, SCI, Scopus
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出版社, 出版地: -
ページ: - 巻号: 253 通巻号: 110525 開始・終了ページ: - 識別子(ISBN, ISSN, DOIなど): CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1879-0836
Publisher: Elsevier