Identifying influential spreaders in complex networks based on entropy weight method and gravity law*

Yan, Xiao-Li; Cui, Yapeng; Ni, Shun-Jiang

doi:10.1088/1674-1056/ab77fe

Cited by 37 publications

(29 citation statements)

References 45 publications

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“…Complex network theory has achieved mature results in many fields such as identification and evaluation of influential nodes [16,17], network risk assessment [18,19], and decisionmaking based on the safety risk suggestions [20,21] in recent years.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Airport Risk Propagation in Chinese Air Transport Network

Zhang

Zhao

Mei

2022

Journal of Advanced Transportation

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In recent years, due to the close coupling between airports, airport risk propagation has become a huge challenge. However, it has not been fully understood on the network level. Airport risk can be transferred through other airports owing to connected resources. In this study, we consider two risk factors including airport delay and saturation and propose a risk coupling model based on a clustering algorithm to fit the index and form risk series. To understand the risk propagation mechanism, we build risk propagation networks based on the Granger Causality test, and we apply complex network theory to analyze the evolution of the risk propagation network. We study the regular pattern of risk propagation from perspectives of time and space. Through network analysis, we find four time stages in the risk propagation process and the participation of airports in risk propagation has a positive correlation with airport sizes. In addition, more large airports tend to prevent risk propagation in unoccupied and normal situations, while small airports perform better than large airports in busy situations. Via the conclusion, our work can assist airlines or air traffic managers in controlling the scale of risk propagation before its key time turning point. By identifying the critical airport level and related factors in risk propagation, they can also reduce single airport risk and risk participation through corresponding risk control measures, finally avoiding the large-scale spread of risk and reducing delay or cancellation of more flights.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of Airport Risk Propagation in Chinese Air Transport Network

Zhang

Zhao

Mei

2022

Journal of Advanced Transportation

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“… Wang et al [36] , propose a measure of influence to quantify the propagation capacity of nodes in complex networks. Yan et al [37] , propose a method that takes into account several aspects of node properties, including local topological characteristics, central location, propagation characteristics, and ownership of neighboring nodes. …”

Section: Related Workmentioning

confidence: 99%

“…Yan et al [37] , propose a method that takes into account several aspects of node properties, including local topological characteristics, central location, propagation characteristics, and ownership of neighboring nodes.…”

Section: Related Workmentioning

confidence: 99%

Identification of COVID-19 Spreaders Using Multiplex Networks Approach

et al. 2020

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In this work, we present a methodology to identify COVID-19 spreaders using the analysis of the relationship between socio-cultural and economic characteristics with the number of infections and deaths caused by the COVID-19 virus in different countries. For this, we analyze the information of each country using the complex networks approach, specifically by analyzing the spreaders countries based on the separator set in 5-layer multiplex networks. The results show that, we obtain a classification of the countries based on their numerical values in socioeconomics, population, Gross Domestic Product (GDP), health and air connections; where, in the spreader set there are those countries that have high, medium or low values in the different characteristics; however, the aspect that all the countries belonging to the separator set share is a high value in air connections. INDEX TERMS Complex networks, complex systems, COVID-19, multiplex networks, optimization, social networks.

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“…Zareie et al [20] proposed an improved clustering ranking approach, which takes the common hierarchical structure of nodes and their neighborhood set into account. Yan et al [21] propose, a new method that considers the local topological characteristics of nodes, center position of nodes, and effect of neighbor nodes.…”

Section: Introductionmentioning

confidence: 99%

Identifying Influential Nodes in Complex Networks Based on Node Itself and Neighbor Layer Information

Zhu

Wang

2021

Symmetry

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Identifying influential nodes in complex networks is of great significance for clearly understanding network structure and maintaining network stability. Researchers have proposed many classical methods to evaluate the propagation impact of nodes, but there is still some room for improvement in the identification accuracy. Degree centrality is widely used because of its simplicity and convenience, but it has certain limitations. We divide the nodes into neighbor layers according to the distance between the surrounding nodes and the measured node. Considering that the node’s neighbor layer information directly affects the identification result, we propose a new node influence identification method by combining degree centrality information about itself and neighbor layer nodes. This method first superimposes the degree centrality of the node itself with neighbor layer nodes to quantify the effect of neighbor nodes, and then takes the nearest neighborhood several times to characterize node influence. In order to evaluate the efficiency of the proposed method, the susceptible–infected–recovered (SIR) model was used to simulate the propagation process of nodes on multiple real networks. These networks are unweighted and undirected networks, and the adjacency matrix of these networks is symmetric. Comparing the calculation results of each method with the results obtained by SIR model, the experimental results show that the proposed method is more effective in determining the node influence than seven other identification methods.

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Identifying influential spreaders in complex networks based on entropy weight method and gravity law*

Cited by 37 publications

References 45 publications

Analysis of Airport Risk Propagation in Chinese Air Transport Network

Analysis of Airport Risk Propagation in Chinese Air Transport Network

Identification of COVID-19 Spreaders Using Multiplex Networks Approach

Identifying Influential Nodes in Complex Networks Based on Node Itself and Neighbor Layer Information

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