title: Percolation and epidemic thresholds in clustered networks.
abstract: We develop a theoretical approach to percolation in random clustered networks. We find that, although clustering in scale-free networks can strongly affect some percolation properties, such as the size and the resilience of the giant connected component, it cannot restore a finite percolation threshold. In turn, this implies the absence of an epidemic threshold in this class of networks, thus extending this result to a wide variety of real scale-free networks which shows a high level of transitivity. Our findings are in good agreement with numerical simulations.
authors.affiliation: School of Informatics, Indiana University, Bloomington, Indiana 47406, USA.
authors.forename: M Angeles
authors.initials: MA
authors.lastname: Serrano
authors.forename: Marián
authors.initials: M
authors.lastname: Boguñá
mesh_terms: D016000:Cluster Analysis; D003198:Computer Simulation; D004196:Disease Outbreaks; D004812:Epidemiologic Methods; D008954:Models, Biological; D015233:Models, Statistical