# Identifying high betweenness centrality nodes in large social networks

## TL;DR

This paper introduces k-path centrality and a randomized algorithm to efficiently identify high betweenness centrality nodes in large social networks, demonstrating improved accuracy and speed.

## Contribution

It presents a novel k-path centrality metric and a scalable randomized algorithm for estimating it, enhancing detection of high betweenness nodes.

## Key findings

- High correlation between k-path centrality and betweenness centrality.
- The algorithm achieves faster execution times than existing methods.
- Experimental results show improved accuracy in identifying key nodes.

## Abstract

This paper proposes an alternative way to identify nodes with high betweenness centrality. It introduces a new metric, k-path centrality, and a randomized algorithm for estimating it, and shows empirically that nodes with high k-path centrality have high node betweenness centrality. The randomized algorithm runs in time $O(\kappa^{3}n^{2-2\alpha}\log n)$ and outputs, for each vertex v, an estimate of its k-path centrality up to additive error of $\pm n^{1/2+ \alpha}$ with probability $1-1/n^2$. Experimental evaluations on real and synthetic social networks show improved accuracy in detecting high betweenness centrality nodes and significantly reduced execution time when compared with existing randomized algorithms.