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Link transmission centrality in large-scale social networks

Qian Zhang 1 Màrton Karsai 2 Alessandro Vespignani 1
2 DANTE - Dynamic Networks : Temporal and Structural Capture Approach
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme, IXXI - Institut Rhône-Alpin des systèmes complexes
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.
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Submitted on : Tuesday, December 11, 2018 - 9:05:46 PM
Last modification on : Wednesday, November 3, 2021 - 5:53:15 AM

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Qian Zhang, Màrton Karsai, Alessandro Vespignani. Link transmission centrality in large-scale social networks. EPJ Data Science, EDP Sciences, 2018, 7 (1), ⟨10.1140/epjds/s13688-018-0162-8⟩. ⟨hal-01952143⟩



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