Vulnerability metrics and analysis for communities in complex networks

Rocco, M S Claudio; Ramírez-Márquez, José Emmanuel

doi:10.1016/j.ress.2011.03.001

Cited by 48 publications

(18 citation statements)

References 18 publications

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“…The second group of models has been proposed for the vulnerability analysis of CIs [1,3,[19][20][21][22][23] to: (1) identify preliminary vulnerabilities of CIs by topology-driven and dynamic analyses; (2) direct and focus further detailed analyses on the most critical areas of the CIs. Complex network models can also be practical in devising and verifying network protection (NP) techniques aimed at hampering the propagation of cascading failures.…”

Section: Introductionmentioning

confidence: 99%

Non-dominated sorting binary differential evolution for the multi-objective optimization of cascading failures protection in complex networks

Sansavini

Zio

2013

Reliability Engineering & System Safety

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A number of research works have been devoted to the optimization of protection strategies (e.g. transmission line switch off) of critical infrastructures (e.g. power grids, telecommunication networks, computer networks, etc) to avoid cascading failures. This work aims at improving a previous optimization approach proposed by some of the authors [1], based on the modified binary differential evolution (MBDE) algorithm. The improvements are three-fold: 1) in the optimization problem formulation, we introduce a third objective function to minimize the impacts of the switching off operations onto the existing network topology; 2) in the optimization problem formulation, we use the final results of cascades, rather than only a short horizon of one step cascading, to evaluate the effects of the switching off strategies; 3) in the optimization algorithm, the fast non-dominated sorting mechanisms are incorporated into the MBDE algorithm: a new algorithm, namely non-dominated sorting binary differential evolution algorithm (NSBDE) is then proposed. The numerical application to the topological structure of the 380 kV Italian power transmission network proves the benefits of the improvements.

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Section: Introductionmentioning

confidence: 99%

Non-dominated sorting binary differential evolution for the multi-objective optimization of cascading failures protection in complex networks

Sansavini

Zio

2013

Reliability Engineering & System Safety

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“…Rocco and Ramirez-Marquez (12) identify sets of network elements that are critical to the connectivity of a network and its communities. Specifically, the paper defines a vulnerability set and value for each of the communities in a complex network.…”

Section: Literature Reviewmentioning

confidence: 99%

The Multiscale Importance of Road Segments in a Network Disruption Scenario: A Risk‐Based Approach

2014

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This article addresses the problem of the multiscale importance of road networks, with the aim of helping to establish a more resilient network in the event of a road disruption scenario. A new model for identifying the most important roads is described and applied on a local and regional scale. The work presented here represents a step forward, since it focuses on the interaction between identifying the most important roads in a network that connect people and health services, the specificity of the natural hazards that threaten the normal functioning of the network, and an assessment of the consequences of three real-world interruptions from a multiscale perspective. The case studies concern three different past events: road interruptions due to a flood, a forest fire, and a mass movement. On the basis of the results obtained, it is possible to establish the roads for which risk management should be a priority. The multiscale perspective shows that in a road interruption the regional system may have the capacity to reorganize itself, although the interruption may have consequences for local dynamics. Coordination between local and regional scales is therefore important. The model proposed here allows for the scaling of emergency response facilities and human and physical resources. It represents an innovative approach to defining priorities, not only in the prevention phase but also in terms of the response to natural disasters, such as awareness of the consequences of road disruption for the rescue services sent out to local communities.

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“…Utilize product inference engine, triangular fuzzifier, and center average defuzzifier, which is shown by Eq. (20) to obtain the weight coefficients.…”

Section: The Vulnerability Of Network: the Value Of Vmentioning

confidence: 99%

“…Recently, many experts and scholars have been working hard on evaluating network vulnerability, and many effective approaches have been proposed . Some methods focus on structures of the networks .…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many experts and scholars have been working hard on evaluating network vulnerability, and many effective approaches have been proposed. [19][20][21] Some methods focus on structures of the networks. [22][23][24] Topological properties, such as connectivity level and the size of largest component, are used to measure network vulnerability.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Evaluation of Network Vulnerability

Zhang

Xia

Wang

et al. 2015

Qual. Reliab. Engng. Int.

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Vulnerability of networks is not only associated with the ability to resist disturbances but also has an impact on stable development of the networks in the long run. In this paper, a new vulnerability evaluation based on fuzzy logics is proposed. To obtain the vulnerability of the networks, fuzzy logic is utilized to model uncertain environment. Therefore, this evaluation can be divided into two steps. One is to use a graph to represent the network and analyze the main properties of the network, including average path length, edge betweenness, degree, and clustering coefficient. The other is to use fuzzy logics according to the main properties. Namely, this step is to calculate deviations, design rule database, and obtain vulnerability. Two examples are given to show the efficiency and practicability of the proposed method at the end. Copyright © 2015 John Wiley & Sons, Ltd.

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Vulnerability metrics and analysis for communities in complex networks

Cited by 48 publications

References 18 publications

Non-dominated sorting binary differential evolution for the multi-objective optimization of cascading failures protection in complex networks

Non-dominated sorting binary differential evolution for the multi-objective optimization of cascading failures protection in complex networks

The Multiscale Importance of Road Segments in a Network Disruption Scenario: A Risk‐Based Approach

Fuzzy Evaluation of Network Vulnerability

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