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How Clustering Affects Epidemics in Random Networks

  • Emilie Coupechoux (a1) and Marc Lelarge (a1)

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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Copyright

Corresponding author

Current address: Laboratoire I3S, CS 40121, Université Nice Sophia Antipolis, 06903 Sophia Antipolis Cedex, France. Email address: coupecho@i3s.unice.fr
∗∗ Postal address: INRIA-ENS, 23 avenue d'Italie, CS 81321, 75214 Paris Cedex 13, France.

Footnotes

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This paper is part of the author's PhD thesis done at INRIA-ENS.

Footnotes

References

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Keywords

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How Clustering Affects Epidemics in Random Networks

  • Emilie Coupechoux (a1) and Marc Lelarge (a1)

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