Research on identification of key nodes in intercity rail networks based on K-shell
LIU Xingyu
LIU Jie
WANG Zhe
LI Haodong
Abstract：This study investigates methods for analyzing network nodes to address the challenge of identify-ing key nodes in intercity rail transit networks. A key node identification method,termed Ks+,is devel-oped. This method integrates the K-shell decomposition method with the influence of neighboring nodes and both dynamic and static network indicators. The model considers static physical indicators such as node degree and shortest path,as well as dynamic operational indicators like hub passenger flow and operational intensity,to compute a comprehensive evaluation value for each node. The ks+value of nodes,indicating their influence within the network,is determined by assessing the global core position through the K-shell decomposition algorithm and evaluating local importance with the influence of neighboring nodes. The effectiveness of this algorithm is demonstrated using the Susceptible-Infectious-Recvered (SIR) model and data from the Yangtze River Delta rail network. The results indicate that the identified key nodes closely correspond to city influence,with the top four nodes being direct-administered municipalities and provincial capitals of the Yangtze River Delta region. Furthermore,the algorithm accurately distinguishes core from non-core nodes,ranking nodes with more critical lines and higher passenger flows higher. Key nodes identified by Ks+exhibit a faster propagation rate in the SIR simulation by 1～3 iterations compared to other algorithms,with a peak passenger loss exceeding by 7％.
Keywords：railway transportationkey nodesKs+algorithmcomplex rail networksgradient boosting algorithm
Publication Date：2024-08-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 181-190 )