Cascading failures in complex networks

Valdez, Lucas D.; Shekhtman, Louis; Rocca, C. E. La; Zhang, Xin; Buldyrev, Sergey V.; Trunfio, Paul; Braunstein, Lidia A.; Havlin, Shlomo

doi:10.1093/comnet/cnaa013

Cited by 51 publications

(43 citation statements)

References 142 publications

(223 reference statements)

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“…Four databases including Scopus, Web of Science, Google Scholars, and Cochran were searched to find other similar review papers based on the applied inclusion/exclusion criteria of this study. Only five review articles turned up [16,[20][21][22]34], yet none of them was a systematic review. In more detail, they did not contain the paper selection criteria and data processing.…”

Section: A Planning the Reviewmentioning

confidence: 99%

“…The critical percolation threshold is the fraction of removed nodes or links leading to the complete collapse of the network [22]. Below the critical threshold, there is no giant connected component, whereas above the critical threshold a giant connected component exists [14].…”

Section: N Percolation Modelsmentioning

confidence: 99%

“…, c) failure spreads from the upper layers to lower layers. Black nodes represent removed nodes, red nodes represent affected nodes, and blue nodes are unaffected nodes (adopted from [22]).…”

Section: ) Percolation Mechanisms Of Failurementioning

confidence: 99%

“…While they fixed the number of dependency links in their model, they varied the degree of nodes based on the number of connectivity links and found that the robustness decreases by having broader degree distribution in layers. Since then, the application of percolation theory for studying and analyzing the robustness of multilayer networks has been expanded, especially for evaluating the effects of different network properties [20,22].…”

Section: Introductionmentioning

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: A Planning the Reviewmentioning

confidence: 99%

Section: N Percolation Modelsmentioning

confidence: 99%

Section: ) Percolation Mechanisms Of Failurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…Keller proposed a new method for embedding network or distance-based data into hyperbolic space [21]. Valdez studied the cascading failures [22] in complex networks and Stegehuis studied the closure coefficients in scale-free complex networks [23]. Literature [24] proposed a method of trajectory tracking on uncertain complex networks.…”

Section: Introductionmentioning

confidence: 99%

Personal Trajectory with Ring Structure Network: Algorithms and Experiments

Liu

Wang

et al. 2021

Security and Communication Networks

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Network theory has provided a new analytical tool for the study of human trajectory and has also achieved rapid development in the complex network field. Conventional network model or complex network model ignores some details and cannot display the most remarkable features for a GPS based personal trajectory. It is necessary to set up a new personal trajectory model. For the purpose of researching the characteristics of trajectory for one person in a long time, we collected a GPS based personal LifeLog dataset named Liu Lifelog in the past 9 years. This paper analyzed the Liu Lifelog and proposed a ring structure personal trajectory (RSPT) model based on the basic complex network model. We discussed the definition, source, characteristic and attribute of the RSPT model and tested the model with the dataset which was provided by the Geolife project and verified that the model described the characteristic of trajectory for a person well. The result shows that this model is feasible and it can predict the human behavior characteristics more accurately and effectively.

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Application of hypergraph theory in the analysis of the failure propagation and diffusion behaviour of machining centre

Zhang

Zhou

2021

Quality & Reliability Eng

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To improve the rationality of failure diagnosis, this research uses hypergraph theory as basis in proposing a method of analysing failure propagation behaviour to study the failure propagation mechanism from the system perspective. A hierarchical structure model is constructed by failure correlation analysis, matrix transformation and decomposition. The probability of failure is determined by considering the influence of multiple truncation data. On this basis, hypergraph theory is used to calculate the one-step and cumulative coefficient of failure propagation and diffusion. The influence degree of failure is calculated by integrating the edge betweenness. Using the influence degree of failure as an indicator, failure propagation and diffusion behaviour are analysed, and critical failure nodes and paths are identified. Lastly, a machining centre is used as an example for specific application. The results are compared with the ranking results of critical failure propagation paths determined by other methods, and the effectiveness of the proposed method is verified.

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Cascading failures in complex networks

Cited by 51 publications

References 142 publications

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

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

Personal Trajectory with Ring Structure Network: Algorithms and Experiments

Application of hypergraph theory in the analysis of the failure propagation and diffusion behaviour of machining centre

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