Link transmission centrality in large-scale social networks

TL;DR

This paper introduces transmission centrality, a new measure for assessing link importance in large social networks based on stochastic diffusion, which helps identify key links that influence spreading processes.

Contribution

The paper proposes a novel transmission centrality measure and an efficient algorithm to compute it, improving identification of influential links in large-scale networks.

Findings

Transmission centrality effectively identifies weak ties that drive spreading.

It outperforms other centrality measures in large empirical social networks.

The method is computationally efficient for large networks.

Abstract

Understanding the importance of links in transmitting information in a network can provide ways to hinder or postpone ongoing dynamical phenomena like the spreading of epidemic or the diffusion of information. In this work, we propose a new measure based on stochastic diffusion processes, the \textit{transmission centrality}, that captures the importance of links by estimating the average number of nodes to whom they transfer information during a global spreading diffusion process. We propose a simple algorithmic solution to compute transmission centrality and to approximate it in very large networks at low computational cost. Finally we apply transmission centrality in the identification of weak ties in three large empirical social networks, showing that this metric outperforms other centrality measures in identifying links that drive spreading processes in a social network.