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      The scaling of human interactions with city size

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          Abstract

          The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail. In this paper, we map society-wide communication networks to the urban areas of two European countries. We show that both the total number of contacts and the total communication activity grow superlinearly with city population size, according to well-defined scaling relations and resulting from a multiplicative increase that affects most citizens. Perhaps surprisingly, however, the probability that an individual's contacts are also connected with each other remains largely unaffected. These empirical results predict a systematic and scale-invariant acceleration of interaction-based spreading phenomena as cities get bigger, which is numerically confirmed by applying epidemiological models to the studied networks. Our findings should provide a microscopic basis towards understanding the superlinear increase of different socioeconomic quantities with city size, that applies to almost all urban systems and includes, for instance, the creation of new inventions or the prevalence of certain contagious diseases.

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          Most cited references24

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          Epidemic Spreading in Scale-Free Networks

          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.
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            Modelling disease outbreaks in realistic urban social networks.

            Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions or ad hoc models for the contact process. Here we explore the use of dynamic bipartite graphs to model the physical contact patterns that result from movements of individuals between specific locations. The graphs are generated by large-scale individual-based urban traffic simulations built on actual census, land-use and population-mobility data. We find that the contact network among people is a strongly connected small-world-like graph with a well-defined scale for the degree distribution. However, the locations graph is scale-free, which allows highly efficient outbreak detection by placing sensors in the hubs of the locations network. Within this large-scale simulation framework, we then analyse the relative merits of several proposed mitigation strategies for smallpox spread. Our results suggest that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population.
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              The Impact of Social Structure on Economic Outcomes

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                Author and article information

                Journal
                Journal of The Royal Society Interface
                J. R. Soc. Interface.
                The Royal Society
                1742-5689
                1742-5662
                September 06 2014
                September 06 2014
                September 06 2014
                : 11
                : 98
                : 20130789
                Affiliations
                [1 ]Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                [2 ]Santa Fe Institute, Santa Fe, NM 87501, USA
                [3 ]Raschke Software Engineering, 65195 Wiesbaden, Germany
                [4 ]British Telecommunications PLC, Ipswich IP5 3RE, UK
                [5 ]Orange Labs, 92794 Issy-les-Moulineaux Cedex 9, France
                Article
                10.1098/rsif.2013.0789
                4233681
                24990287
                ab6fca30-bad1-4891-bd34-2bd98143a29a
                © 2014

                https://royalsociety.org/journals/ethics-policies/data-sharing-mining/

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