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A New k-shell decomposition Method for Identifying Influential Spreaders of Epidemics on Community Networks

Journal of Systems Science and Information 2018年第4期9卷 366-375页

作者： kai GONG Li kANG School of Economic Information Engineering Southwestern University of Finance and Economics Collaborative Innovation Center for the Innovation and Regulation of Internet-based Finance Southwestern University of Finance and Economics Laboratory for Financial Intelligence and Financial Engineering Southwestern University of Finance and Economics

An efficient method for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its community structures and by the heterogeneous distributions of out-leaving links among nodes bridging over communities. A new method for community networks capable of identifying influential spreaders that accelerate the spread of disease is here proposed. In this method, influential spreaders serve as target nodes. This is based on the idea that, in k-shell decomposition method,out-leaving links and inner links are processed separately. The method was used on empirical networks constructed from online social networks, and results indicated that this method is more accurate. Its effectiveness stems from the patterns of connectivity among neighbors, and it successfully identified the important nodes. In addition, the performance of the method remained robust even when there were errors in the structure of the network.

关键词： complex networks community structure k-shell decomposition influential spreaders

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Identifying Vital Nodes on Temporal Networks: An Edge-Based k-shell decomposition

Identifying Vital Nodes on Temporal Networks: An Edge-Based ...

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第36届中国控制会议

作者： Zhanghui Ye Xiuxiu Zhan Yinzuo Zhou Chuang Liu Zi-ke Zhang Alibaba Research Center for Complexity Sciences Hangzhou Normal University

ISBN: (纸本)9781538629185

There is an ever-increasing interest in studying temporal networks nowadays, as temporal networks can illustrate the real-world system more accurately. To date, how to characterize nodes’ importance is still unclear in temporal networks. In this work, we first use a time window graph model to cut the temporal network into slices, and then we give an indicator for network centrality according to the edge-based k-shell decomposition for the temporal networks, which is named as temporal k-shell decomposition. We mainly use the size of the largest component after removing the nodes with large centrality value to test the method’s performance. The numerical experiments on several real networks indicate that the temporal k-shell method outperforms some other indicators, and the results with different time window size show that the improvement is also robust.

关键词： Temporal Network Time Window Graph Model k-shell decomposition Robustness

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The spatiotemporal analysis of the population migration network in China,

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Infectious Disease Modelling 2023年第4期8卷 1117-1126页

作者： Wenjie Li Ye Yao Department of Biostatistics School of Public HealthFudan UniversityShanghaiChina key Laboratory of Public Health Safety of Ministry of Education Fudan UniversityShanghaiChina

Population migration is a critical component of large-scale spatiotemporal models of infectious disease *** the most influential spreaders in networks is vital to controlling and understanding the spreading process of infectious *** used Baidu Migration data for the whole year of 2021 to build mobility *** nodes of the network represent cities,and the edges represent the population flow between *** applying the k-shell decomposition and the Louvain algorithm,we could get the k-shell values for each city and community ***,we identified the most efficient nodes or pathways in a complex network by generating random ***,we analyzed the eigenvalue of the migration matrix to find the nodes that have the most impact on the *** also found the consistency between k-shell value and eigenvalue through kendall's t *** main result is that in Spring Festival and National Day,the network is at higher risk of an infectious disease outbreak and the Yangtze River Delta is at the highest risk of an epidemic all year *** is the most significant node in both k-shell value and eigenvalue *** spatiotemporal property of the network should be taken into account to model the transmission of infectious diseases more accurately.

关键词： k-shell decomposition Louvain algorithm Population mobility Infectious disease Network analysis

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