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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Fast influencers in complex networks
Organization Unit
  • Fang Zhou
  • Linyuan Lü
  • Manuel Sebastian Mariani
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Communications in Nonlinear Science and Numerical Simulation
Publisher Elsevier
Geographical Reach international
ISSN 1007-5704
Volume 74
Page Range 69 - 83
Date 2019
Abstract Text Influential nodes in complex networks are typically defined as those nodes that maximize the asymptotic reach of a spreading process of interest. However, for practical applications such as viral marketing and online information spreading, one is often interested in maximizing the reach of the process in a short amount of time. The traditional definition of influencers in network-related studies from diverse research fields narrows down the focus to the late-time state of the spreading processes, leaving the following question unsolved: which nodes are able to initiate large-scale spreading processes, in a limited amount of time? Here, we find that there is a fundamental difference between the nodes – which we call “fast influencers” – that initiate the largest-reach processes in a short amount of time, and the traditional, “late-time” influencers. Stimulated by this observation, we provide an extensive benchmarking of centrality metrics with respect to their ability to identify both the fast and late-time influencers. We find that local network properties can be used to uncover the fast influencers. In particular, a parsimonious, local centrality metric (which we call social capital) achieves optimal or nearly-optimal performance in the fast influencer identification for all the analyzed empirical networks. Local metrics tend to be also competitive in the traditional, late-time influencer identification task.
Digital Object Identifier 10.1016/j.cnsns.2019.01.032
Other Identification Number merlin-id:17704
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