The role of bridge nodes between layers on epidemic spreading

Real networks, like the international airport network and the Internet, are composed of interconnected layers (or communities) through a small fraction of nodes that we call here ‘bridge nodes’. These nodes are crucial in the spreading of epidemics because they enable the spread the disease to the entire system. In this workwe study the effect of the bridge nodes on the susceptibleinfected-recoveredmodel in a two layer networkwith a small fraction r of these nodes. In the dynamical process, we theoretically determine that at criticality and for the limit r→0, the time tb at which thefirst bridge node is infected diverges as a power-lawwith r, while above criticality, it appears a crossover between a logarithmic and a power-law behavior. Additionally, in the steady state at criticality, the fraction of recovered nodes scales with r as a power-lawwhose exponent can be understood from the finite size cluster distribution at criticality.We also test ourmodel on the real international airline network and show that ‘high-degree bridge nodes’ reduce the time tb.

[1]  Joel C Miller,et al.  Edge-based compartmental modelling for infectious disease spread , 2011, Journal of The Royal Society Interface.

[2]  Joel C. Miller,et al.  A primer on the use of probability generating functions in infectious disease modeling , 2018, Infectious Disease Modelling.

[3]  Alain Barrat,et al.  Global disease spread: statistics and estimation of arrival times. , 2008, Journal of theoretical biology.

[4]  Michael Gardam,et al.  Questioning Aerosol Transmission of Influenza , 2007, Emerging infectious diseases.

[5]  Dawei Zhao,et al.  Statistical physics of vaccination , 2016, ArXiv.

[6]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[7]  C. Buono,et al.  Epidemics in Partially Overlapped Multiplex Networks , 2013, PloS one.

[8]  P. A. Macri,et al.  Crossover from weak to strong disorder regime in the duration of epidemics , 2012, 1212.6384.

[9]  Joel C. Miller,et al.  Mathematics of Epidemics on Networks: From Exact to Approximate Models , 2017 .

[10]  P. Leath Cluster size and boundary distribution near percolation threshold , 1976 .

[11]  R. Rothenberg,et al.  Using Social Network and Ethnographic Tools to Evaluate Syphilis Transmission , 1998, Sexually transmitted diseases.

[12]  Simon Cauchemez,et al.  Chains of transmission and control of Ebola virus disease in Conakry, Guinea, in 2014: an observational study. , 2015, The Lancet. Infectious diseases.

[13]  Lidia A. Braunstein,et al.  Temporal Percolation of the Susceptible Network in an Epidemic Spreading , 2012, PloS one.

[14]  M. Newman,et al.  Random graphs with arbitrary degree distributions and their applications. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Mason A. Porter,et al.  Author Correction: The physics of spreading processes in multilayer networks , 2016, 1604.02021.

[16]  Reuven Cohen,et al.  Complex Networks: Structure, Robustness and Function , 2010 .

[17]  Hans J. Herrmann,et al.  Revealing the structure of the world airline network , 2014, Scientific Reports.

[18]  Wei Wang,et al.  Unification of theoretical approaches for epidemic spreading on complex networks , 2016, Reports on progress in physics. Physical Society.

[19]  J. Quastel Diffusion in Disordered Media , 1996 .

[20]  D. Stauffer Scaling Theory of Percolation Clusters , 1979, Complex Media and Percolation Theory.

[21]  Ming Tang,et al.  Effects of weak ties on epidemic predictability on community networks , 2012, Chaos.

[22]  Sangwook Kim,et al.  Identifying and ranking influential spreaders in complex networks by neighborhood coreness , 2014 .

[23]  Dietrich Stauffer,et al.  Introduction To Percolation Theory , 2018 .

[24]  H. Stanley,et al.  A test of scaling near the bond percolation threshold , 1978 .

[25]  Reuven Cohen,et al.  Percolation critical exponents in scale-free networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Conrado J. Pérez Vicente,et al.  Diffusion dynamics on multiplex networks , 2012, Physical review letters.

[27]  Lixin Tian,et al.  Resilience of networks with community structure behaves as if under an external field , 2018, Proceedings of the National Academy of Sciences.

[28]  E. Monberg,et al.  Percolation and cluster distribution. II. layers, variable-range interactions, and exciton cluster model , 1978 .

[29]  R. Guimerà,et al.  The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Ming Tang,et al.  Identify influential spreaders in complex networks, the role of neighborhood , 2015, ArXiv.

[31]  Douglas Cochran,et al.  Conjoining Speeds up Information Diffusion in Overlaying Social-Physical Networks , 2011, IEEE Journal on Selected Areas in Communications.

[32]  M. Newman Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  S. Havlin,et al.  Fractals and Disordered Systems , 1991 .

[34]  Piet Van Mieghem,et al.  Epidemic processes in complex networks , 2014, ArXiv.

[35]  S. Havlin,et al.  Spontaneous repulsion in the A+B→0 reaction on coupled networks , 2018, Physical review. E.

[36]  L. Meyers Contact network epidemiology: Bond percolation applied to infectious disease prediction and control , 2006 .

[37]  Kezan Li,et al.  A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks , 2017 .

[38]  Mason A. Porter,et al.  Multilayer networks , 2013, J. Complex Networks.