Node importance identification of unweighted urban rail transit network: An Adjacency Information Entropy based approach

Wencheng Huang, Haoran Li, Yanhui Yin, Zhi Zhang, Anhao Xie, Yin Zhang and Guo Cheng

Reliability Engineering and System Safety, 2024, vol. 242, issue C

Abstract: Inspiring by the theory of degree entropy, and considering both the location of the evaluated node and its neighboring nodes in an unweighted urban rail transit network (URTN), a new node identification approach called Adjacency Information Entropy (AIE) is applied to identify the importance of node in URTN. An undirected and unweighted network, a single-way directed and unweighted network, and a double-way directed and unweighted network are constructed as the background of the numerical study, some other previous approaches are used as the comparison algorithms. Finally, based on the double-way directed and unweighted network topology of Chengdu Metro, a real-world case study is conducted. We find that: (i) For a node in a directed and unweighted network, as long as the in-degree and out-degree of a node are not both 0, then the node can be identified based on AIE. (ii) For a double-way directed and unweighted network, if a node has higher node degree and higher Adjacency Degree, then it is more important in the network. (iii) If a node has high AIE in the entire topology of URTN, then it generates connections among non-adjacent nodes.

Keywords: Node importance identification; Unweighted urban rail transit network; Adjacency Information Entropy; ABC classification method; Network performance (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:242:y:2024:i:c:s0951832023006804

DOI: 10.1016/j.ress.2023.109766

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