Leveraging Random-Walk Hitting Times for Influential Spreaders Identification

One of the most studied problems in network science is the identification of those nodes that, once activated, maximize the fraction of nodes that are reached by a spreading process of interest. In parallel, scholars have introduced network effective distances as topological metrics to estimate the hitting time of diffusive spreading processes. Here, we connect the two problems – the influential spreaders identification and spreading processes’ hitting time estimation – by introducing a centrality metric, called ViralRank, which quantifies how close a node is, on average, to the other nodes in terms of the random-walk effective distance. We show that ViralRank significantly outperforms state-of-the-art centrality metrics in identifying influential spreaders for super-critical contact-network processes and for metapopulation global contagion processes. Our findings deepen our understanding of the influential spreaders identification problem, and reveal how reliable diffusion hitting-time estimates contribute to its solution.

Authors: 
Flavio Iannelli, Manuel Sebastian Mariani and Igor M. Sokolov
Room: 
2
Date: 
Monday, September 24, 2018 - 18:15 to 18:30

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