Temporal Betweenness Centrality on Shortest Paths

TL;DR

This paper improves the computational efficiency of calculating temporal betweenness centrality in graphs by introducing a more efficient algorithm with better time complexity.

Contribution

The paper presents a new algorithm that reduces the running time for computing temporal betweenness centrality in graphs.

Findings

New algorithm with O(n m T + n^2 T) complexity

Improved efficiency over previous methods

Applicable to large temporal graphs

Abstract

Betweenness centrality measure assesses the importance of nodes in a graph and has been used in a variety of contexts. Betweenness centrality has also been extended to temporal graphs. Temporal graphs have edges that bear labels according to the time of the interactions between the nodes. Betweenness centrality has been extended to the temporal graph settings, and the notion of paths has been extended to temporal paths. Recent results by Bu{\ss} et al. and Rymar et al. showed that the betweenness centrality of all nodes in a temporal graph can be computed in O(n^3 T^2) or O(n^2 m T^2 ), where T is the number of time units, m the number of temporal edges and n the number of nodes. In this paper, we improve the running time analysis of these previous approaches to compute the betweenness centrality of all nodes in a temporal graph. We give an algorithm that runs in O(n m T + n^2 T ).