Evaluating the robustness of temporal networks considering spatiality of connections

Lu, Xi; Wang, Hongping; Deng, Yong

doi:10.1016/j.chaos.2015.07.017

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…To unveil how the network characteristics can enhance or reduce resilience, Gao et al [22] proposed a dimensionality reduction method, which can collapse the multidimensional coupled nonlinear equations onto a single universal resilience function. Although the above study provided a universal resilience analysis framework [23], [24], [25], it neglected the inherent capability of UAVs. In fact, a single UAV still contributes to the cooperative mission of the system under self-organization in FANETs.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the temporal and spatial characteristics of the FANET, it is difficult to model the topology of the network. One method is to model the evolution of network topology as a Markov process to quantify the time-varying topology with a stationary distribution [25], [27]. When UAVs are destroyed continuously, the network structure consisting of remaining nodes and edges changes.…”

Section: Spatio-temporal Network Model Of a Fanetmentioning

confidence: 99%

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Resilience Analysis of Cooperative Mission Based on Spatio–Temporal Network Dynamics for Flying Ad Hoc Network

Guo,

Wang,

Sun

et al. 2024

IEEE Trans. Rel.

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Due to possible disturbances in a conflict environment, the mission performance of flying ad hoc networks (FANET) is highly related to time-varying topology. Currently, the resilience metric is regarded as suitable for evaluating the mission performance of a FANET with time-varying network topology. However, the reported resilience studies contribute little to the theoretical analysis of the impact of the time-varying topology of FANET on a cooperative mission. In this article, an improved resilience analysis method is proposed by defining a new dimension reduction operator, which takes into account the impacts of both isolated and communicated unmanned aerial vehicles. Then, based on random geometry theory, the topological features of FANET are extracted to measure the structure perturbation and recovery. A case study of a joint reconnaissance mission shows that our analysis method is more realistic than the typical method. Our results may be helpful to analyze the resilience of networked dynamic systems and support the more flexible topology control methods design of a FANET.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Spatio-temporal Network Model Of a Fanetmentioning

confidence: 99%

Resilience Analysis of Cooperative Mission Based on Spatio–Temporal Network Dynamics for Flying Ad Hoc Network

Guo,

Wang,

Sun

et al. 2024

IEEE Trans. Rel.

View full text Add to dashboard Cite

show abstract

“…The relationship between two nodes like diseases spreading [15,16] and information dissemination [17] in real-world networks has important significance [18].…”

Section: Introductionmentioning

confidence: 99%

Identifying influential nodes based on fuzzy local dimension in complex networks

Wen

Jiang

2019

Chaos, Solitons & Fractals

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How to identify influential nodes in complex networks is an important aspect in the study of complex network. In this paper, a novel fuzzy local dimension (FLD) is proposed to rank the influential nodes in complex networks, where a node with high fuzzy local dimension has high influential ability. This proposed method focuses on the influence of the distance from the center node on the local dimension of center node by fuzzy set, resulting in a change in influential ability. In order to show this proposed method's effectiveness and accuracy, four real-world networks are applied in this paper. Meanwhile, Susceptible-Infected (SI) is used to simulate the spreading process by FLD and other centrality measures, and Kendall's tau coefficient is used to describe the correlation between the influential nodes obtained by centrality and the results measured by SI model. Observing from the ranking lists and simulated results, this method is effective and accurate to rank the influential nodes.

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Review on R&D task integrated management of intelligent manufacturing equipment

Ren

Luo

et al. 2022

Neural Comput & Applic

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Evaluating the robustness of temporal networks considering spatiality of connections

Cited by 4 publications

References 40 publications

Resilience Analysis of Cooperative Mission Based on Spatio–Temporal Network Dynamics for Flying Ad Hoc Network

Resilience Analysis of Cooperative Mission Based on Spatio–Temporal Network Dynamics for Flying Ad Hoc Network

Identifying influential nodes based on fuzzy local dimension in complex networks

Review on R&D task integrated management of intelligent manufacturing equipment

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