Three faces of node importance in network epidemiology: Exact results for small graphs

We investigate three aspects of the importance of nodes with respect to susceptible-infectious-removed (SIR) disease dynamics: influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size), and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that (i) node separation is more important than centrality for more than one active node, (ii) vaccination and influence maximization are the most different aspects of importance, and (iii) the three aspects are more similar when the infection rate is low.

[1]  T. Greenhalgh 42 , 2002, BMJ : British Medical Journal.

[2]  Ulrik Brandes,et al.  Smallest graphs with distinct singleton centers , 2014, Netw. Sci..

[3]  Petter Holme,et al.  Cost-efficient vaccination protocols for network epidemiology , 2016, PLoS Comput. Biol..

[4]  Brendan D. McKay,et al.  Practical graph isomorphism, II , 2013, J. Symb. Comput..

[5]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

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

[7]  Stefan Richter,et al.  Centrality Indices , 2004, Network Analysis.

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

[9]  Claudio Castellano,et al.  Fundamental difference between superblockers and superspreaders in networks , 2016, Physical review. E.

[10]  Ramon Huerta,et al.  Contact tracing and epidemics control in social networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  S. Borgatti,et al.  The centrality of groups and classes , 1999 .

[12]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[13]  Duanbing Chen,et al.  Vital nodes identification in complex networks , 2016, ArXiv.

[14]  William B. Hart,et al.  Fast Library for Number Theory: An Introduction , 2010, ICMS.

[15]  Petter Holme,et al.  Model Versions and Fast Algorithms for Network Epidemiology , 2014, 1403.1011.

[16]  Jari Saramäki,et al.  Ranking influential spreaders is an ill-defined problem , 2017, EPL (Europhysics Letters).

[17]  Mario Vento,et al.  An Improved Algorithm for Matching Large Graphs , 2001 .

[18]  Ulrik Brandes,et al.  Network Analysis: Methodological Foundations , 2010 .

[19]  Alessandro Vespignani,et al.  Immunization of complex networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  S. Janson,et al.  Graphs with specified degree distributions, simple epidemics, and local vaccination strategies , 2007, Advances in Applied Probability.

[22]  N. Christakis,et al.  Social Network Sensors for Early Detection of Contagious Outbreaks , 2010, PloS one.

[23]  Herbert W. Hethcote,et al.  The Mathematics of Infectious Diseases , 2000, SIAM Rev..

[24]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[25]  Alessandro Vespignani,et al.  Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. , 2007, Journal of theoretical biology.

[26]  Alain Barrat,et al.  Optimizing surveillance for livestock disease spreading through animal movements , 2012, Journal of The Royal Society Interface.

[27]  Jimeng Sun,et al.  A Survey of Models and Algorithms for Social Influence Analysis , 2011, Social Network Data Analytics.

[28]  Naoki Masuda,et al.  Directionality of contact networks suppresses selection pressure in evolutionary dynamics. , 2008, Journal of theoretical biology.

[29]  Andy R. Terrel,et al.  SymPy: Symbolic computing in Python , 2017, PeerJ Prepr..