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December 2014 How clustering affects epidemics in random networks
Emilie Coupechoux, Marc Lelarge
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Adv. in Appl. Probab. 46(4): 985-1008 (December 2014). DOI: 10.1239/aap/1418396240

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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Emilie Coupechoux. Marc Lelarge. "How clustering affects epidemics in random networks." Adv. in Appl. Probab. 46 (4) 985 - 1008, December 2014. https://doi.org/10.1239/aap/1418396240

Information

Published: December 2014
First available in Project Euclid: 12 December 2014

zbMATH: 1323.60020
MathSciNet: MR3290426
Digital Object Identifier: 10.1239/aap/1418396240

Subjects:
Primary: 60C05
Secondary: 05C80, 91D30

Rights: Copyright © 2014 Applied Probability Trust

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Vol.46 • No. 4 • December 2014
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