Node importance evaluation in aviation network based on “No Return” node deletion method

Wen, Xiaodong; Tu, Congliang; Wu, Minchen

doi:10.1016/j.physa.2018.02.109

Cited by 29 publications

(15 citation statements)

References 35 publications

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“…Wen et al, 2018 [38] Proposed a node importance ranking based on so-called no-return nodes. Results on the Chinese airport network (199 nodes) and the US airport network (332 nodes and 2126 links) were reported.…”

Section: Clemente Et Al 2019 [37]mentioning

confidence: 99%

Robustness of Air Transportation as Complex Networks:Systematic Review of 15 Years of Research and Outlook into the Future

Sun

Wandelt

2021

Sustainability

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Air transportation systems are an important part of the critical infrastructure in our connected world. Accordingly, a better understanding and improvements in the resilience of the overall air transportation system are essential to the well-functioning of our society and overall sustainability of human beings. In the literature, network science is increasingly used to better understand the resilience dynamics of air transportation. Given the wide application of tools for network science and the importance of designing resilient air transportation systems, a rich body of studies has emerged in recent years. This review paper synthesizes the related literature that has been published throughout the last 15 years regarding the robustness of air transportation systems. The contributions of this work consist of two major elements. The first part provides a comprehensive discussion and cross-comparison of the reported results. We cover several major topics, including node importance identification, failure versus attack profiles, recovery and improvement techniques, and networks of networks approaches. The second part of this paper complements the review of aggregated findings by elaborating on a future agenda for robust air transportation research. Our survey-style overview hopefully contributes toward a better understanding of the state of the art in this research area, and, in turn, to the improvement of future air transportation resilience and sustainability.

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Section: Clemente Et Al 2019 [37]mentioning

confidence: 99%

Robustness of Air Transportation as Complex Networks:Systematic Review of 15 Years of Research and Outlook into the Future

Sun

Wandelt

2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…It can find the key nodes with faster energy consumption and important position in the network, which is of great significance to enhance the invulnerability and prolong the life of the network. On the basis of a "no return" node deletion method, reference [20] uses the network efficiency, largest component size, and network flow as the indicators of network performance. Evaluating node importance is done by comparing the change of network overall performance before and after deleting nodes.…”

Section: Related Workmentioning

confidence: 99%

Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

Yin

Cui

et al. 2019

J Wireless Com Network

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Identifying important nodes is very crucial to design efficient communication networks or contain the spreading of information such as diseases and rumors. The problem is formulated as follows: given a network, which nodes are the more important? Most current studies did not incorporate the structure change as well as application features of a network. Aiming at the node importance evaluation in wireless sensor networks, a new method which ranks nodes according to their structural importance and performance impact is proposed. Namely, this method considers two aspects of the network, network structural characteristics and application requirements. This method integrates four indicators which reflect the node importance, namely, node degree, number of spanning trees, delay, and network energy consumption. Firstly, the changes in the four indicators are analyzed using the node deletion method. Then, the TOPSIS multi-attribute decision-making method is applied to merge these four evaluation indicators. On this basis, a more comprehensive evaluation method (MADME) for node importance is obtained. Theory study reveals MADME method saves computational time. And the simulation results show the superiority of the MADME method over various algorithms such as the N-Burt method, betweenness method, DEL-Node method, and IE-Matrix method. The accuracy of the evaluation can be improved, and the key nodes determined by the MADME method have a more obvious effect on the network performance. Our method can provide guidance on influential node identification in the network.

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“…Since a safe operation of the metro depends heavily on the orderly cooperation of multisystems and elements, metro-operation accidents tend to be increased by multiple interacted hazards occurred following certain sequences or in the form of a network. In recent years, the network node importance (NNI) evaluation has been viewed as an effective tool for prioritizing the nodes or factors of a networked system [3], which has been successfully used to investigate, e.g., traffic networks [4,5], power grids [6], and social networks [7,8]. However, the traditional NNI evaluation reliant on indicators of network topology (e.g., degree centrality and betweenness centrality) is capable of examining the physical or social networks for whom connectivity and accessibility are predominate attributes of the nodes, but are insufficient for explaining networks containing causal relations such as accident networks.…”

Section: Introductionmentioning

confidence: 99%

Causation of Metro Operation Accidents in China: Calculation of Network Node Importance Based on DEMATEL and ISM

et al. 2021

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Continuous metro-operation accidents lead to serious economic loss and a negative social impact. The accident causation analysis is of great significance for accident prevention and metro operation safety promotion. Network node importance (NNI) evaluation has been widely used as a tool for ranking the nodes in complex networks; however, traditional indicators such as degree centrality (DC) are insufficient for examining accident networks. This study proposed an improved method by integrating decision making trail and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) into traditional NNI evaluation, where the key nodes are determined by both the nature of the accident network topology and the contribution of the nodes to accident development. Drawing on this method, 32 accident causal factors were identified and prioritized on the ground of 248 accident cases. It was found that 14 important factors related to staff (e.g., “driver noncompliance”), environment (e.g., “extrinsic nature disturbance”), passenger (e.g., “passenger sudden illness”), and machine (e.g., “track failures”) should be given priority in safety management due to their significant tendency of causing metro accidents. Theoretical and managerial implications were discussed to provide useful insights into the understanding of the causation of metro accidents and form a basis for metro managers to develop targeted safety countermeasures related to metro operation. The proposed hybrid method is proven effective in investigating accident networks involving sequential and casual relationships and revealing factors with high possibility to increase accidents.

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Node importance evaluation in aviation network based on “No Return” node deletion method

Cited by 29 publications

References 35 publications

Robustness of Air Transportation as Complex Networks:Systematic Review of 15 Years of Research and Outlook into the Future

Robustness of Air Transportation as Complex Networks:Systematic Review of 15 Years of Research and Outlook into the Future

Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

Causation of Metro Operation Accidents in China: Calculation of Network Node Importance Based on DEMATEL and ISM

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