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Browsing Mathematics & Statistics by Author "Gleeson, James P."

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Browsing Mathematics & Statistics by Author "Gleeson, James P."

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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 ...
  • Fennell, Peter G; Gleeson, James P.; Cellai, Davide (American Physical Society, 2014)
    Facilitated spin models were introduced some decades ago to mimic systems characterized by a glass transition. Recent developments have shown that a class of facilitated spin models is also able to reproduce characteristic ...
  • Gleeson, James P.; Melnik, Sergey (American Physical Society, 2009)
    An analytical approach to calculating bond percolation thresholds, sizes of k-cores, and sizes of giant connected components on structured random networks with nonzero clustering is presented. The networks are generated ...
  • Gleeson, James P. (American Physical Society, 2013)
    A wide class of binary-state dynamics on networks-including, for example, the voter model, the Bass diffusion model, and threshold models-can be described in terms of transition rates (spin-flip probabilities) that depend ...
  • Gleeson, James P. (American Physical Society, 2009)
    Analytical results are derived for the bond percolation threshold and the size of the giant connected component in a class of random networks with nonzero clustering. The network's degree distribution and clustering spectrum ...
  • Hackett, Adam W. (University of Limerick, 2011)
    The network topologies on which many natural and synthetic systems are built provide ideal settings for the emergence of complex phenomena. One well-studied manifestation of this, called a cascade or avalanche, is observed ...
  • 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. (American Physical Society, 2002)
    Recently Vlad et al. [Phys. Rev. E. 63, 066304 (2001)] applied the method of decorrelation trajectories to the transport of tracers in stochastic velocity fields with constant drift, and found that the average ...
  • 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 ...
  • Cellai, Davide; Lawlor, Aonghus; Dawson, Kenneth A; Gleeson, James P. (American Physical Society, 2013)
    k-core percolation is a percolation model which gives a notion of network functionality and has many applications in network science. In analyzing the resilience of a network under random damage, an extension of this model ...
  • 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, 2002)
    A quadrature expression is derived for the probability density function of passive tracers advected from a point by a one-dimensional, single-scale, Gaussian velocity field. The effect of trapping on the tracer moments and ...
  • 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.; Melnik, Sergey; Hackett, Adam W. (American Physical Society, 2010)
    The question of how clustering (nonzero density of triangles) in networks affects their bond percolation threshold has important applications in a variety of disciplines. Recent advances in modeling highly clustered networks ...
  • Goulding, D; Melnik, S; Curtin, D; Piwonski, T; Houlihan, J; Gleeson, James P.; Hayet, G (American Physical Society, 2007)
    no abstract available
  • 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 ...
  • Villiers, Rory (University of Limerick, 2011)
    The most common method of pricing a cashflow collateralized debt obligation (cashflow CDO) is to use Monte Carlo integration. However, Monte Carlo integration is computationally intensive and often faster methods of ...

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