Epidemic spreading in scale-free networks.

By Romualdo Pastor-Satorras, Alessandro Vespignani. Physical Review Letters, 2000

5,460 citations recorded by the source corpus.

The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and persistence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8886371eeec632b29b445975a18be21b46a8f8f8

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In Statistical and Nonlinear Physics

Citations in the corpus

Analysis of a large-scale weighted network of one-to-one human communication (2007, background): “For such a rapidly decaying degree distribution the hubs are few, and therefore many properties of traditional scale-free networks, from anomalous diffusion [15] to error tolerance [16], are absent.”
Analysis and Control of Epidemics: A Survey of Spreading Processes on Complex Networks (2015, background): “Many of these models are created by first looking at empirical data of a spreading process like AIDS [146] or a computer virus [69] and then determining what type of model and how many states should be used to capture its behavior.”
The Anatomy of a Scientific Rumor (2013, background): “Conversely, heterogeneous structures like scale-free networks require heterogeneous mean-field approximation [23, 24], involving the single-site equation governing the time evolution of the relative density of “infected” vertices with given connectivity k, i.”
Selection, Tinkering, and Emergence in Complex Networks - Crossing the Land of Tinkering (2002, background): “The spread of computer viruses, for example, is closely related to the epidemic patterns displayed by natural viruses [85].”
A Model of Large-Scale proteome Evolution (2002, background): “The implications of these topologies are enormous also for our understanding of epidemics [30,24].”
Discrete-time Markov chain approach to contact-based disease spreading in complex networks (2009, methodology): “Specifically, if β is the rate (probability per unit time) at which the disease spreads, it follows that the epidemic threshold in uncorrelated SF networks is given [8] by βc = 〈k〉/〈k (2)〉, leading to βc → 0 as N → ∞ when 2 < γ ≤ 3.”
Statistical physics of vaccination (2016, methodology): “Another related measure of correlations is the average degree of the nearest neighbors of vertices of degree k, denoted by k̄nn(k) which is formally defined as [145]”
Exact controllability of complex networks (2013, background): “While great effort has been devoted to understanding the interplay between complex networks and dynamical processes taking place on them [1–5] in various natural and technological systems [6–16], the control of complex dynamical networks remains to be an outstanding problem.”
Statistical physics of social dynamics (2007, background): “Similarly, we do not review the intense recent activity on epidemics spreading (Anderson and May, 1991; Lloyd and May, 2001) on networks (May, 2006; Pastor-Satorras and Vespignani, 2001), though we devote a section to social spreading phenomena.”