Efficient Link-Based Spatial Network Disintegration Strategy

IF 6.7 2区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY
Zhigang Wang;Ye Deng;Ze Wang;Jürgen Kurths;Jun Wu
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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.
基于链接的高效空间网络分解策略
许多真实的复杂系统,如基础设施和互联网,不是随机的,而是嵌入在度量空间中。空间网络解体问题,即关键区域识别问题,是网络科学的一个基础性研究领域,近年来受到越来越多的关注。典型的应用包括网络免疫、流行病控制和解体预警信号。由于计算难度大(NP-hard),它们通常不能用多项式算法来解决。本文提出了一种基于链路的空间网络分解方法。首先,我们引入了空间网络的多解体圈区域失效模型。然后,我们计算圈内链接的特定属性值之和,以确定空间网络的关键区域,这些区域也对应于发生解体的地理区域。在不同类型的真实网络上进行的大量实验表明,该策略在解决质量方面优于传统方法。
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来源期刊
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering
CiteScore
12.60
自引率
9.10%
发文量
393
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.
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