Percolation in multilayer complex networks with connectivity and interdependency topological structures

Cao, Yan-Yun; Liu, Run-Ran; Jia, Chun-Xiao; Wang, Bing–Hong

doi:10.1016/j.cnsns.2020.105492

Cited by 23 publications

(14 citation statements)

References 58 publications

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“…The classic approach considers such impacts (or attacks) as an accidental (unintentional) occurrence. The corresponding models are the error tolerance model [7]; "forest fire"-based models [8], including the one with immune trees [9]; demon model [10]; cellular automata-based model [11,12]; and random attack percolation models [13,14]. The latter ones, including the targeted attack percolation models [15][16][17][18], localized attack percolation models [19], and k-core percolation [20][21][22][23], are also used to simulate the intentional attacks on a network's nodes and edges, assuming that the Attacker tries to disrupt the network connectivity as quickly as possible.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical Problems of Managing the Risks of Complex Systems under Targeted Attacks with Known Structures

Shiroky

Kalashnikov

2021

Mathematics

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This paper deals with the problem of managing the risks of complex systems under targeted attacks. It is usually solved by using Defender–Attacker models or similar ones. However, such models do not consider the influence of the defending system structure on the expected attack outcome. Our goal was to study how the structure of an abstract system affects its integral risk. To achieve this, we considered a situation where the Defender knows the structure of the expected attack and can arrange the elements to achieve a minimum of integral risk. In this paper, we consider a particular case of a simple chain attack structure. We generalized the concept of a local risk function to account for structural effects and found an ordering criterion that ensures the optimal placement of the defending system’s elements inside a given simple chain structure. The obtained result is the first step to formulate the principles of optimally placing system elements within an arbitrarily complex network. Knowledge of these principles, in turn, will allow solving the problems of optimal allocation of resources to minimize the risks of a complex system, considering its structure.

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Section: Introductionmentioning

confidence: 99%

“…The obtained results are the step towards deriving the optimal placement criteria for arbitrary structures. (1) Random attack percolation models [13,14] Error tolerance model [7] "Forest fire" (FF) [8] based models: FF with immune trees [9], Demon model [10],…”

Section: Introductionmentioning

confidence: 99%

Mathematical Problems of Managing the Risks of Complex Systems under Targeted Attacks with Known Structures

Shiroky

Kalashnikov

2021

Mathematics

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“…According to the collected papers in this study, more layers have a devastating effect on the robustness of the multilayer network [44,54,59,61,76]. However, Radicchi et al [88] showed that in a network with a large number of layers adding a new layer can boost the robustness since it can create redundant interdependencies among layers.…”

Section: E the Effect Of Number Of Layersmentioning

confidence: 85%

“…In [50,61], weighted links were defined as the spatial length of connectivity and dependency links while [53][54][55] defined tolerate parameter as the weight of dependency links between layers. More information regarding the tolerance parameter can be found in Section F.…”

Section: Linksmentioning

confidence: 99%

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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“…Recent research on the vulnerability of power systems has primarily focused on the CPS vulnerability analysis of the power grid. Studies on the vulnerability of the power grid structure were generally based on the complex network theory, especially the analysis and verification of small‐world (SW) characteristics and scale‐free characteristics in power systems 9,10,11,12,13 . However, some scholars focused on the vulnerability analysis of ADNs 5,6,14 .…”

Section: Introductionmentioning

confidence: 99%

Robustness assessment of the cyber‐physical system against cascading failure in a virtual power plant based on complex network theory

Gao

Yang

2021

Int Trans Electr Energ Syst

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Summary The combination of a virtual power plant (VPP) and an active distribution network (ADN) can be used to aggregate distributed energy resources of prosumers. The bi‐directional power flow and information flow between prosumers ensure close coupling and interdependence of the power system and the cyber system so that the VPP gradually evolves into a typical cyber‐physical system (CPS). However, even a small cyber attack on the CPS system significantly affects the security and integrity of the VPP. To study robustness of CPS in the VPP, a novel stochastic cascading failure model based on the complex network theory is introduced, considering the impact of various interdependencies and network relationships. First, the centralized and decentralized multi‐agent system (MAS) structures in the VPP are investigated, and the fault propagation mechanism and cascading failure process of the CPS network are established. Secondly, a simple and effective comparison method is proposed to analyze the different network relationships and coupling interdependencies. Thirdly, A new multiple‐to‐multiple inter‐links addition strategy is proposed to enhance the protection of key nodes, and mainly focus on researching the intra‐ and inter‐links addition sensitivity analysis. The numerical simulation shows that optimal network relationship, coupling interdependency, and intra‐links addition strategy are determined by the proposed robustness metrics.

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Percolation in multilayer complex networks with connectivity and interdependency topological structures

Cited by 23 publications

References 58 publications

Mathematical Problems of Managing the Risks of Complex Systems under Targeted Attacks with Known Structures

Mathematical Problems of Managing the Risks of Complex Systems under Targeted Attacks with Known Structures

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

Robustness assessment of the cyber‐physical system against cascading failure in a virtual power plant based on complex network theory

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