Immunization of complex networks.

By Romualdo Pastor-Satorras, Alessandro Vespignani. Physical review. E, Statistical, nonlinear, and soft matter physics, 2001

1,172 citations recorded by the source corpus.

Complex networks such as the sexual partnership web or the Internet often show a high degree of redundancy and heterogeneity in their connectivity properties. This peculiar connectivity provides an ideal environment for the spreading of infective agents. Here we show that the random uniform immunization of individuals does not lead to the eradication of infections in all complex networks. Namely, networks with scale-free properties do not acquire global immunity from major epidemic outbreaks even in the presence of unrealistically high densities of randomly immunized individuals. The absence of any critical immunization threshold is due to the unbounded connectivity fluctuations of scale-free networks. Successful immunization strategies can be developed only by taking into account the inhomogeneous connectivity properties of scale-free networks. In particular, targeted immunization schemes, based on the nodes' connectivity hierarchy, sharply lower the network's vulnerability to epidemic attacks.

Subfields: Ecology, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/45103412abce218fb6346ab82879dcbcde8de1dd

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Citations in the corpus

Statistical physics of vaccination (2016, background): “The simplest vaccination strategy that one can conceive, the implementation of which requires no preparation or information at all, is uniform or random vaccination [271, 275].”
Epidemics and immunization in scale‐free networks (2002, methodology): “This feature reverberates also in the choice of immunization strategies [20, 21] and changes radically the standard epidemiological framework usually adopted in the description and characterization of disease propagation.”
Human disease classification in the postgenomic era: A complex systems approach to human pathobiology (2007, background): “Scale-free networks also minimize the consequences of most biochemical or genetic errors (Wagner and Fell, 2001), and accommodate perturbations to the network with minimal effects on critical functions (Pastor-Satorras and Vespignani, 2002), unless hubs are the targets of the perturbations (Albert, ”
Leaders in Social Networks, the Delicious Case (2011, background): “On the other hand, effective ranking algorithm may serve its role to identify influential users for immunization and stop epidemic outbreak [15].”
The Structure and Function of Complex Networks (2003, background): “A good discussion has been given by Pastor-Satorras and Vespignani [335].”
Identifying influential spreaders in complex networks (2010, positive): “In the case of random networks, it is found that viruses propagate to the entire network above an epidemic threshold given by β > β rand c ≡ λ h k i / h k 2 [24, 26].”
The structure and dynamics of multilayer networks (2014, background): “A great number of immunization scenarios, such as targeted immunization [264], ring immunization [265], and acquaintance immunization [266], have been proposed, but the majority of them, based on the premise of compulsory requirements, completely neglects the individuals’ willingness and desires, wh”
Graph theory and networks in Biology. (2006, background): “This also follows from the following formula for the epidemic threshold for scale-free networks with degree distribution P (k) ∼ k−3, which was presented in [240] (as well as a number of other sources).”
Imitation dynamics of vaccination behaviour on social networks (2011, background): “The vaccination of hubs can play a vital role in containing infections (Pastor-Satorras & Vespignani 2002), and public health programs often try to promote herd immunity by allocating vaccinations preferentially to these hubs (Bansal et al. 2006).”
Social Network Sensors for Early Detection of Contagious Outbreaks (2010, background): “enhance the population-level efficacy of a prophylactic intervention ( 9-12 ) and that optimal placement of sensors in physical networks (such as water pumping stations) could detect outbreaks sooner.”
Identifying influential nodes in complex networks (2012, background): “How to find these influential nodes is of theoretical significance.”
The H-index of a network node and its relation to degree and coreness (2016, background): “…the critical nodes associated with the speciﬁc dynamics of large-scale networks 11 will allow us to better control the outbreak of epidemics 12 , conduct successful advertisements for e-commercial products 13 , prevent catastrophic outages in power grids or the Internet 14 , optimize the…”
Cost-effective outbreak detection in networks (2007, background): “Several strategies for immunization have also been proposed: uniform node immunization, targeted immunization of high degree nodes [20] and acquaintance immunization, which focuses on highly connected nodes [5].”
Controlling edge dynamics in complex networks (2011, background): “Therefore, hubs have an important role not only in maintaining the connectivity of a network in case of random failures [5,15,24] and containing epidemic spreading [50, 51], but they also make it possible to control the network efficiently with a smaller number of driver nodes.”