Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-26T05:09:27.183Z Has data issue: false hasContentIssue false

The Effect of Graph Structure on Epidemic Spread in a Class ofModified Cycle Graphs

Published online by Cambridge University Press:  24 April 2014

Get access

Abstract

In this paper, an SIS (susceptible-infected-susceptible)-type epidemic propagation isstudied on a special class of 3-regular graphs, called modified cycle graphs. The modifiedcycle graph is constructed from a cycle graph with N nodes by connecting nodei to thenode i +d in a way that every node has exactly three links.Monte-Carlo simulations show that the propagation process depends on the value ofd in anon-monotone way. A new theoretical model is developed to explain this phenomenon. Thisreveals a new relation between the spreading process and the average path length in thegraph.

Type
Research Article
Copyright
© EDP Sciences, 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

A. Barrat, M. Barthelemy, A. Vespignani. Dynamical Processes on Complex Networks. Cambridge University Press, Cambridge, 2008.
Boguna, M., Pastor–Satorras, R.. Epidemic spreading in correlated complex networks. Phys. Rev. E., 66 (2002), 47104. CrossRefGoogle ScholarPubMed
L. Danon, A.P. Ford, T. House, C.P. Jewell, M.J. Keeling, G.O. Roberts, J.V. Ross, M.C. Vernon. Networks and the Epidemiology of Infectious Disease, Interdisciplinary Perspectives on Infectious Diseases. 2011:284909 special issue “Network Perspectives on Infectious Disease Dynamics”.
Gleeson, J.P.. High-accuracy approximation of binary-state dynamics on networks. Phys. Rev. Letters, 107 (2011), 068701. CrossRefGoogle Scholar
House, T., Keeling, M. J.. Insights from unifying modern approximations to infections on networks. J. Roy. Soc. Interface, 8 (2011), 67-73. CrossRefGoogle Scholar
Keeling, M.J., Eames, K.T.D.. Networks and epidemic models. J. Roy. Soc. Interface, 2 (2005), 295-307. CrossRefGoogle ScholarPubMed
Nagy, N., Simon, P.L.. Monte-Carlo simulation and analytic approximation of epidemic processes on large networks. Central European Journal of Mathematics, 11 (4) (2013), 800-815. Google Scholar
Newman, M. E. J.. The structure and function of complex networks. SIAM Review, 45 (2003), 167-256. CrossRefGoogle Scholar
Saramäki, J., Kaski, K.. Modelling development of epidemics with dynamic small-world networks. J. Theor. Biol., 234 (2005), 413-421. CrossRefGoogle Scholar
Simon, P.L., Taylor, M., Kiss, I.Z.. Exact epidemic models on graphs using graph automorphism driven lumping. J. Math. Biol., 62 (2010), 479-508. CrossRefGoogle Scholar
Taylor, M., Simon, P.L., Green, D. M., House, T., Kiss, I.Z.. From Markovian to pairwise epidemic models and the performance of moment closure approximations. J. Math. Biol., 64 (2012), 1021-1042. CrossRefGoogle Scholar
H.C. Tijms. A First Course in Stochastic Models. John Wiley and Sons, 2003.