Intrinsic Correlation with Betweenness Centrality and Distribution of Shortest Paths

Yelai Feng, Huaixi Wang, Chao Chang and Hongyi Lu
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Yelai Feng: College of Electronic Engineering, National University of Defense Technology, Hefei 230000, China
Huaixi Wang: College of Electronic Engineering, National University of Defense Technology, Hefei 230000, China
Chao Chang: College of Electronic Engineering, National University of Defense Technology, Hefei 230000, China
Hongyi Lu: College of Computer, National University of Defense Technology, Changsha 410000, China

Mathematics, 2022, vol. 10, issue 14, 1-18

Abstract: Betweenness centrality evaluates the importance of nodes and edges in networks and is one of the most pivotal indices in complex network analysis; for example, it is widely used in centrality ordering, failure cascading modeling, and path planning. Existing algorithms are based on single-source shortest paths technology, which cannot show the change of betweenness centrality with the growth of paths, and prevents deep analysis. We propose a novel algorithm that calculates betweenness centrality hierarchically and accelerates computing via GPUs. Based on the novel algorithm, we find that the distribution of shortest path has an intrinsic correlation with betweenness centrality. Furthermore, we find that the betweenness centrality indices of some nodes are 0, but these nodes are not edge nodes, and they characterize critical significance in real networks. Experimental evidence shows that betweenness centrality is closely related to the distribution of the shortest paths.

Keywords: network science; graph theory; betweenness centrality; shortest path distribution (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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