Structural analysis and the sum of nodes' betweenness centrality in complex networks
Abstract
Structural analysis in network science is finding the information hidden from the topology structure of complex networks. Many methods have already been proposed in the research on the structural analysis of complex networks to find the different structural information of networks. In this work, the sum of nodes' betweenness centrality (SBC) is used as a new structural index to check how the structure of the complex networks changes in the process of the network's growth. We build two four different processes of network growth to check how the structure change will be manifested by the SBC. We find that when the networks are under BarabásiAlbert rule, the value of SBC for each network grows like a logarithmic function. However, when the rule that guides the network's growth is the ErdősRényi rule, the value of SBC will converge to a fixed value. It means the rules that guide the network's growth can be illustrated by the change of the SBC in the process of the network's growth. In other words, in the structure analysis of complex networks, the sum of nodes' betweenness centrality can be used as an index to check what kinds of rules guide the network's growth.
 Publication:

arXiv eprints
 Pub Date:
 February 2024
 DOI:
 10.48550/arXiv.2402.13507
 arXiv:
 arXiv:2402.13507
 Bibcode:
 2024arXiv240213507D
 Keywords:

 Physics  Physics and Society;
 Physics  Data Analysis;
 Statistics and Probability