Betweenness Centrality : Algorithms and Lower Bounds

TL;DR

This paper introduces a randomized parallel algorithm and an algebraic approach for computing betweenness centrality in large networks, along with a lower bound proof for path-comparison algorithms.

Contribution

It presents novel algorithms for betweenness centrality computation and establishes a theoretical lower bound on their complexity.

Findings

Proposed a randomized parallel algorithm for betweenness centrality.

Developed an algebraic method for the same problem.

Proved a lower bound of O(nm) for path-comparison based algorithms.

Abstract

One of the most fundamental problems in large scale network analysis is to determine the importance of a particular node in a network. Betweenness centrality is the most widely used metric to measure the importance of a node in a network. In this paper, we present a randomized parallel algorithm and an algebraic method for computing betweenness centrality of all nodes in a network. We prove that any path-comparison based algorithm cannot compute betweenness in less than O(nm) time.