{"title":"Efficient Link-Based Spatial Network Disintegration Strategy","authors":"Zhigang Wang;Ye Deng;Ze Wang;Jürgen Kurths;Jun Wu","doi":"10.1109/TNSE.2024.3523952","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1096-1111"},"PeriodicalIF":6.7000,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Network Science and Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10824926/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.