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

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 undi-rected 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.

Automated summary

Inspired by the theory of degree entropy, this study proposes a new node identification approach called Adjacency Information Entropy (AIE) to identify the importance of nodes in urban rail transit networks (URTN). Through numerical and real-world case studies, it is found that AIE can effectively identify important nodes and facilitate connections among non-adjacent nodes.