Link transmission centrality in large-scale social networks

Qian Zhang, Márton Karsai and Alessandro Vespignani


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.

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