EPJ Data Sci. (2018) 7: 33
https://doi.org/10.1140/epjds/s13688-018-0162-8

Regular article

Link transmission centrality in large-scale social networks

¹ Laboratory for the Modeling Biological and Socio-technical Systems, Northeastern University, Boston, USA
² LIP UMR 5668, IXXI, Université de Lyon, ENS de Lyon, Inria, CNRS, UCB Lyon 1, Lyon, France

^* e-mail: qianzhang@northeastern.edu

Received: 14 February 2018
Accepted: 31 August 2018
Published online: 14 September 2018

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 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.