Advances in Applied Probability

How clustering affects epidemics in random networks

Emilie Coupechoux and Marc Lelarge

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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.

Article information

Source
Adv. in Appl. Probab., Volume 46, Number 4 (2014), 985-1008.

Dates
First available in Project Euclid: 12 December 2014

Permanent link to this document
https://projecteuclid.org/euclid.aap/1418396240

Digital Object Identifier
doi:10.1239/aap/1418396240

Mathematical Reviews number (MathSciNet)
MR3290426

Zentralblatt MATH identifier
1323.60020

Subjects
Primary: 60C05: Combinatorial probability
Secondary: 05C80: Random graphs [See also 60B20] 91D30: Social networks

Keywords
Contagion threshold diffusion random graph clustering

Citation

Coupechoux, Emilie; Lelarge, Marc. How clustering affects epidemics in random networks. Adv. in Appl. Probab. 46 (2014), no. 4, 985--1008. doi:10.1239/aap/1418396240. https://projecteuclid.org/euclid.aap/1418396240


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