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Browsing MACSI - Mathematics Application Consortium for Science & Industry by Subject "epidemics"

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Browsing MACSI - Mathematics Application Consortium for Science & Industry by Subject "epidemics"

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  • Gleeson, James P.; Melnik, Sergey; Ward, Jonathan A; Porter, Mason A; Murcha, Peter J (American Physical Society, 2012)
    Mean-field analysis is an important tool for understanding dynamics on complex networks. However, surprisingly little attention has been paid to the question of whether mean-field predictions are accurate, and this is ...
  • Hackett, Adam W.; Melnik, Sergey; Gleeson, James P. (American Physical Society, 2011)
    We present an analytical approach to determining the expected cascade size in a broad range of dynamical models on the class of random networks with arbitrary degree distribution and nonzero clustering introduced previously ...
  • Gleeson, James P. (American Physical Society, 2011)
    Binary-state dynamics (such as the susceptible-infected-susceptible (SIS) model of disease spread, or Glauber spin dynamics) on random networks are accurately approximated using master equations. Standard mean-field and ...
  • Melnik, Sergey; Hackett, Adam W.; Porter, Mason A; Mucha, Peter J; Gleeson, James P. (American Physical Society, 2011)
    We demonstrate that a tree-based theory for various dynamical processes operating on static, undirected networks yields extremely accurate results for several networks with high levels of clustering. We find that such a ...

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