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      Cooperative spreading processes in multiplex networks

      1,2 , 1 , 1,3 , 4 , 1

      Chaos

      AIP Publishing LLC

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          Abstract

          This study is concerned with the dynamic behaviors of epidemic spreading in multiplex networks. A model composed of two interacting complex networks is proposed to describe cooperative spreading processes, wherein the virus spreading in one layer can penetrate into the other to promote the spreading process. The global epidemic threshold of the model is smaller than the epidemic thresholds of the corresponding isolated networks. Thus, global epidemic onset arises in the interacting networks even though an epidemic onset does not arise in each isolated network. Simulations verify the analysis results and indicate that cooperative spreading processes in multiplex networks enhance the final infection fraction.

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          Most cited references 42

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          Epidemic spreading in scale-free networks

          The Internet, as well as many other networks, 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 prevalence 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 rationalize data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.
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            The spread of epidemic disease on networks

             M. Newman (2002)
            The study of social networks, and in particular the spread of disease on networks, has attracted considerable recent attention in the physics community. In this paper, we show that a large class of standard epidemiological models, the so-called susceptible/infective/removed (SIR) models can be solved exactly on a wide variety of networks. In addition to the standard but unrealistic case of fixed infectiveness time and fixed and uncorrelated probability of transmission between all pairs of individuals, we solve cases in which times and probabilities are non-uniform and correlated. We also consider one simple case of an epidemic in a structured population, that of a sexually transmitted disease in a population divided into men and women. We confirm the correctness of our exact solutions with numerical simulations of SIR epidemics on networks.
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              The Web of Human Sexual Contacts

              Many ``real-world'' networks are clearly defined while most ``social'' networks are to some extent subjective. Indeed, the accuracy of empirically-determined social networks is a question of some concern because individuals may have distinct perceptions of what constitutes a social link. One unambiguous type of connection is sexual contact. Here we analyze data on the sexual behavior of a random sample of individuals, and find that the cumulative distributions of the number of sexual partners during the twelve months prior to the survey decays as a power law with similar exponents \(\alpha \approx 2.4\) for females and males. The scale-free nature of the web of human sexual contacts suggests that strategic interventions aimed at preventing the spread of sexually-transmitted diseases may be the most efficient approach.
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                Author and article information

                Journal
                Chaos
                Chaos
                CHAOEH
                Chaos
                AIP Publishing LLC
                1054-1500
                1089-7682
                June 2016
                14 June 2016
                14 June 2016
                : 26
                : 6
                Affiliations
                [1 ]School of Mathematics and Statistics, Wuhan University , Wuhan, Hubei 430072, China
                [2 ]Department of Engineering, Honghe University , Honghe, Yunnan 661100, China
                [3 ]Computational Science Hubei Key Laboratory, Wuhan University , Wuhan 430072, China
                [4 ]School of Mathematics and Statistics, South-Central University for Nationalities , Wuhan 430074, China
                Author notes
                [a)]

                Electronic mail: shcheng@ 123456whu.edu.cn

                [b)]

                Electronic mail: xqwu@ 123456whu.edu.cn

                Article
                1.4952964 005698CHA 16133NMSSR
                10.1063/1.4952964
                7112487
                27368800
                Copyright © 2016 Author(s)

                Published by AIP Publishing.

                1054-1500/2016/26(6)/065311/7/ $30.00

                All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/ ).

                Page count
                Pages: 7
                Funding
                Funded by: National Natural Science Foundation of China (NSFC) http://dx.doi.org/10.13039/501100001809
                Award ID: 41301442
                Award ID: 61273215
                Award ID: 61573262
                Categories
                Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems
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