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Browsing MACSI - Mathematics Application Consortium for Science & Industry by Title

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  • 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 ...
  • Hackett, Adam W.; Gleeson, James P. (American Physical Society, 2013)
    We present an analytical approach to determining the expected cascade size in a broad range of dynamical models on the class of highly clustered random graphs introduced by Gleeson [J. P. Gleeson, Phys. Rev. E 80, 036107 ...
  • Gleeson, James P.; Ward, Jonathan A; O'Sullivan, Kevin P; Lee, William T. (American Physical Society, 2014)
    Heavy-tailed distributions of meme popularity occur naturally in a model of meme diffusion on social networks. Competition between multiple memes for the limited resource of user attention is identified as the mechanism ...
  • Khoury, Maria; Gleeson, James P.; Sancho, J. M.; Lacasta, A. M.; Lindenberg, Katja (American Physical Society, 2009)
    Transport and diffusion of particles on modulated surfaces is a nonequilibrium problem which is receiving a great deal of attention due to its technological applications, but analytical calculations are scarce. In earlier ...
  • Melnik, Sergey; Porter, Mason A; Mucha, Peter J; Gleeson, James P. (American Institute of Physics, 2014)
    We develop a new ensemble of modular random graphs in which degree-degree correlations can be different in each module, and the inter-module connections are defined by the joint degree-degree distribution of nodes for each ...
  • 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 ...
  • Gleeson, James P. (American Physical Society, 2008)
    The mean size of unordered binary avalanches on infinite directed random networks may be determined using the damage propagation function introduced by [B. Samuelsson and J. E. S. Socolar, Phys. Rev. E 74, 036113 (2006)]. ...
  • Melnik, Sergey; Ward, Jonathan A; Gleeson, James P.; Porter, Mason A (American Institute of Physics, 2013)
    The spread of ideas across a social network can be studied using complex contagion models, in which agents are activated by contact with multiple activated neighbors. The investigation of complex contagions can provide ...
  • Cellai, Davide; López, Eduardo; Zhou, Jie; Gleeson, James P.; Bianconi, Ginestra (American Physical Society, 2013)
    From transportation networks to complex infrastructures, and to social and communication networks, a large variety of systems can be described in terms of multiplexes formed by a set of nodes interacting through different ...
  • Vynnycky, Michael; Masuda, Y (American Institute of Physics, 2013)
    The problem of fast viscous steady Rayleigh-Bénard convection in a rectangular enclosure is revisited using asymptotic and numerical methods. There are two generic cases: in the first, there is zero shear stress at all ...
  • Kyrke-Smith, T.M; Katz, R.F; Fowler, Andrew (Interntional Glaciological Society, 2013)
    One challenge in improving our understanding of ice-stream dynamics is to develop models of the spatial and temporal transition from ice-sheet to ice-stream flow. We address this with a new, vertically integrated, higher-order ...
  • Gleeson, James P. (2009)
    The motion of overdamped particles in a one-dimensional spatially-periodic potential is considered. The potential is also randomly-fluctuating in time, due to multiplicative colored noise terms, and has a deterministic ...
  • 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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