Cascading Failure Model for Command and Control Networks Based on an <i>m</i>-Order Adjacency Matrix

Wang, Yun-Ming; Chen, Bo; Chen, Xiaoshuang; Gao, Xiue

doi:10.1155/2018/6404136

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Random immunity is also called uniform immunity. A part of the nodes in the network are randomly selected for immunization [34,35]. The chance of each node being immunized is equal, and it does not vary according to the level of the risk of infection.…”

Section: Direct Immunementioning

confidence: 99%

DR-SCIR Public Opinion Propagation Model with Direct Immunity and Social Reinforcement Effect

Guo

Wang

et al. 2020

Symmetry

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The DR-SCIR network public opinion propagation model was employed to study the characters of S-state users stopping transmitting information for the first time and secondary transmission of immune users. The model takes into account symmetry and complexity such as direct immunization and social reinforcement effect, proposes the probability of direct immunity Psr and the probability of transform from the immune state to the hesitant state Prc, and divides public opinion information into positive public opinion and negative public opinion based on whether the public opinion information is confirmed. Simulation results show that, when direct immunity Psr = 0.5, the density of I-state nodes in the model decreased by 54.12% at the peak index; when the positive social reinforcement effect factor b = 10, the density of I-state nodes in the model increased by 16.67% at the peak index; and when the negative social reinforcement effect factor b = -10, the density of I-state nodes in the model decreased by 55.36% at the peak index. It shows that increasing the positive social reinforcement effect factor b can promote the spread of positive public opinion, reducing the negative social reinforcement effect factor b can control the spread of negative public opinion, and direct immunization can effectively suppress the spread of public opinion. This model can help us better analyze the rules of public opinion on social networks, so as to maintain a healthy and harmonious network and social environment.

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Section: Direct Immunementioning

confidence: 99%

DR-SCIR Public Opinion Propagation Model with Direct Immunity and Social Reinforcement Effect

Guo

Wang

et al. 2020

Symmetry

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“…The above research did not consider the complexity of node attributes in the real world, and the different general meanings of nodes are difficult to represent in lower-order network models. Wang et al [16] used a high-order complex network model to analyze the robustness of a bus-subway composite network and found that the composite network is better than the single network. Sun et al [17] considered the actual traffic flow and adopted a residual capacity allocation strategy to construct a cascading failure model for composite transportation networks.…”

Section: Introductionmentioning

confidence: 99%

Robustness Analysis of an Urban Public Traffic Network Based on a Multi-Subnet Composite Complex Network Model

Sun

2023

Entropy

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An urban public traffic network is a typical high-order complex network. There are multiple types of transportation in an urban public traffic network, and each type has different impacts on urban transportation. Robustness analyses of urban public traffic networks contribute to the safe maintenance and operation of urban traffic systems. In this paper, a new cascading failure model for urban public traffic networks is constructed based on a multi-subnet composite complex network model. In order to better simulate the actual traffic flow in the composite network, the concept of traffic function is proposed in the model. Considering the different effects of various relationships on nodes in the composite network, the traditional cascading failure model has been improved and a deliberate attack strategy and a random attack strategy have been adopted to study the robustness of the composite network. In the experiment, the urban bus–subway composite network in Qingdao, China, was used as an example for simulation. The experimental results showed that under two attack strategies, the network robustness did not increase with the increase in capacity, and the proportion of multiple relationships had a significant impact on the network robustness.

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“…A wide range of real-world systems such as power grids [1], financial transaction networks [2], communication networks (e.g., the Internet) [3], and command and control systems [4] have been modeled as complex networks. Among other characteristics, the resiliency of networked systems has received growing research attention from diverse application areas including economic systems [5], organizational management [6], and multiple engineering systems [7,8].…”

Section: Introductionmentioning

confidence: 99%

Dynamics and resiliency of networks with concurrent cascading failure and self-healing

2022

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Local attacks in networked systems can often propagate and trigger cascading failures. Designing effective healing mechanisms to counter cascading failures is critical to enhance system resiliency. This work proposes a self-healing algorithm for networks undergoing load-based cascading failure. To advance understanding of the dynamics of networks with concurrent cascading failure and self-healing, a general discrete-time simulation framework is developed, and the resiliency is evaluated using two metrics, i.e., the system impact and the recovery time. This work further explores the effects of the multiple model parameters on the resiliency metrics. It is found that two parameters (reactivated node load parameter and node healing certainty level) span a phase plane for network dynamics where three regimes exist. To ensure full network recovery, the two parameters need to be moderate. This work lays the foundation for subsequent studies on optimization of model parameters to maximize resiliency, which will have implications to many real-world scenarios.

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Cascading Failure Model for Command and Control Networks Based on an m-Order Adjacency Matrix

Cited by 7 publications

References 37 publications

DR-SCIR Public Opinion Propagation Model with Direct Immunity and Social Reinforcement Effect

DR-SCIR Public Opinion Propagation Model with Direct Immunity and Social Reinforcement Effect

Robustness Analysis of an Urban Public Traffic Network Based on a Multi-Subnet Composite Complex Network Model

Dynamics and resiliency of networks with concurrent cascading failure and self-healing

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