A Lookahead algorithm to compute Betweenness Centrality

TL;DR

This paper introduces a lookahead algorithm that efficiently computes Betweenness Centrality, significantly reducing computation time compared to repeatedly applying the Brandes algorithm, especially in dynamic network scenarios.

Contribution

A novel lookahead algorithm for Betweenness Centrality that outperforms repeated Brandes computations in dynamic network contexts.

Findings

Achieves faster Betweenness Centrality computation than Brandes algorithm.

Effective in scenarios with sequential vertex removals.

Extensible to general cases beyond the specific algorithm.

Abstract

The Betweenness Centrality index is a very important centrality measure in the analysis of a large number of networks. Despite its significance in a lot of interdisciplinary applications, its computation is very expensive. The fastest known algorithm presently is by Brandes which takes O(|V || E|) time for computation. In real life scenarios, it happens very frequently that a single vertex or a set of vertices is sequentially removed from a network. The recomputation of Betweenness Centrality on removing a single vertex becomes expensive when the Brandes algorithm is repeated. It is to be understood that as the size of the network increases, Betweenness Centrality calculation becomes more and more expensive and even a decrease in running time by a small fraction results in a phenomenal decrease in the actual running time. The algorithm introduced in this paper achieves the same in a significantly lesser time than repetition of the Brandes algorithm. The algorithm can also be extended to a general case.