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Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks

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  • Li, Zhitao
  • Tang, Jinjun
  • Zhao, Chuyun
  • Gao, Fan

Abstract

An increasing number of studies have attempted to build a multiplex network (MN) model to generalize the traditional network theory and have proposed various centrality measures for MNs. In this paper, we proposed an improved centrality measure, the Adaptive PageRank Algorithm Modified by the Gravity Model (APAMGM), to identify critical nodes in MNs. We modified the idea that the transition probability of a node is only related to its outgoing connections (or degree in an undirected network) in the adapted PageRank algorithm. In APAMGM, the transition probability is positively correlated with the quality of nodes and inversely correlated with the interaction impedance between nodes. We conducted a case study using a multiplex urban transportation network in Shenzhen, China, which consists of a bus, metro, taxi, and shared bike network. The results show that APAGMG can identify critical nodes with good interpretability and it displays potential for application in networks where spatial interactions exist between nodes. The interdependencies in the network were explored and discussed with the characterization of nodes. This study might provide insights into applying complex network theory and centrality measures to some MNs, especially urban transportation networks.

Suggested Citation

  • Li, Zhitao & Tang, Jinjun & Zhao, Chuyun & Gao, Fan, 2023. "Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:chsofr:v:167:y:2023:i:c:s0960077922011778
    DOI: 10.1016/j.chaos.2022.112998
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