How clustering affects epidemics in random networks

Emilie Coupechoux 1 Marc Lelarge 2, 1, 3
1 DYOGENE - Dynamics of Geometric Networks
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : Motivated by the analysis of social networks, we study a model of random networks that has both a given degree distribution and a tunable clustering coefficient. We consider two types of growth process on these graphs that model the spread of new ideas, technologies, viruses, or worms: the diffusion model and the symmetric threshold model. For both models, we characterize conditions under which global cascades are possible and compute their size explicitly, as a function of the degree distribution and the clustering coefficient. Our results are applied to regular or power-law graphs with exponential cutoff and shed new light on the impact of clustering.
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Submitted on : Saturday, January 24, 2015 - 11:29:18 PM
Last modification on : Thursday, October 17, 2019 - 12:36:04 PM

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Emilie Coupechoux, Marc Lelarge. How clustering affects epidemics in random networks. Advances in Applied Probability, Applied Probability Trust, 2014, 46 (4), pp.985-1008. ⟨10.1239/aap/1418396240⟩. ⟨hal-01109159⟩

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