Research on the connection radius of dependency links in interdependent spatial networks against cascading failures

Dong, Zhengcheng; Tian, Meng; Liang, Jiaqi; Fang, Yanjun; Lu, Yuxin

doi:10.1016/j.physa.2018.09.007

Cited by 9 publications

(16 citation statements)

References 31 publications

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“…Synthetic networks including Erdös-Rényi network (ER), scale-free network (SF), random regular network (RR), square lattice network (SL), and small world network (SW) were applied in the papers to define the configuration of nodes and links within layers. In [39], SF and SL were combined so that all nodes of two SF networks were randomly located in a 2D square plane with a defined side length.…”

Section: B Network Typementioning

confidence: 99%

“…Similarly, it is much more common that a central telecommunication center depends on a central power station [26]. There are two linking patterns in correlated interdependence; assortative and disassortative [39,49].…”

Section: F Form Of Interdependencementioning

confidence: 99%

“…Some studies only considered one of these interdependency forms in their models while some others studied how the combinations of these forms affect the robustness of multilayer networks. [39,[49][50][51][52] built their models with the combination of full and correlated interdependence. [53,55] considered partially tolerated interdependence among layers.…”

Section: F Form Of Interdependencementioning

confidence: 99%

“…Embedding nodes in specific locations is a fundamental quantity to characterize the structure of infrastructure systems and impose some spatial constraints for example because of geographical and topographical features [42]. On the other hand, different distributions of link length reflect different characteristics in infrastructure systems like highways, transmission lines, communication lines and pipelines [20,39] where the vulnerability of the network depends on the critical length of links [63]. Co-location is another spatialrelated feature implying that two networks have an effect on each other due to locating at a close distance.…”

Section: Spatialitymentioning

confidence: 99%

“…In this way, core/root nodes are picked randomly or trajectorially, depicting the center of the damage. Then, all nodes/links within the specified distance from the core are removed abruptly [61], or gradually (shell by shell) according to increasing distance [37,39,44]. Dong et al [37] proposed a framework to model probabilistic attacks to analysis the system performance under post hazard scenarios.…”

Section: ) Attack Typesmentioning

confidence: 99%

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Network Properties for Robust Multilayer Infrastructure Systems: A Percolation Theory Review

2021

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Infrastructure systems, such as power, transportation, telecommunication, and water systems, are composed of multiple components which are interconnected and interdependent to produce and distribute essential goods and services. So, the robustness of infrastructure systems to resist disturbances is critical for the durable performance of modern societies. Multilayer networks have been used to model the multiplicity and interrelation of infrastructure systems and percolation theory is the most common approach to quantify the robustness of such networks. This survey systematically reviews literature published between 2010 and 2021, on applying percolation theory to assess the robustness of infrastructure systems modeled as multilayer networks. We discussed all network properties applied to build infrastructure models. Among all properties, interdependency strength and communities were the most common network property whilst very few studies considered realistic attributes of infrastructure systems such as directed links and feedback conditions. The review highlights that the properties produced approximately similar model outcomes, in terms of detecting improvement or deterioration in the robustness of multilayer infrastructure networks, with few exceptions. Most of the studies focused on highly simplified synthetic models rather than models built by real datasets. Thus, this review suggests analyzing multiple properties in a single model to assess whether they boost or weaken the impact of each other. In addition, the effect size of different properties on the robustness of infrastructure systems should be quantified. It can support the design and planning of robust infrastructure systems by arranging and prioritizing the most effective properties.

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Section: B Network Typementioning

confidence: 99%