Link transmission centrality in large-scale social 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.
Type de document :
Pré-publication, Document de travail
19 pages, 5 figures. 2018
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https://hal.inria.fr/hal-01831482
Contributeur : Márton Karsai <>
Soumis le : jeudi 5 juillet 2018 - 21:53:59
Dernière modification le : mercredi 23 janvier 2019 - 19:48:12

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  • HAL Id : hal-01831482, version 1
  • ARXIV : 1802.05337

Citation

Qian Zhang, Márton Karsai, Alessandro Vespignani. Link transmission centrality in large-scale social networks. 19 pages, 5 figures. 2018. 〈hal-01831482〉

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