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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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  • Faqeeh, Ali; Osat, Saeed; Radicchi, Filippo; Gleeson, James P. (2019)
    Experimental and computational studies provide compelling evidence that neuronal systems are characterized by power-law distributions of neuronal avalanche sizes. This fact is interpreted as an indication that these systems ...
  • Starnini, Michele; Gleeson, James P.; Boguna, Marián (American Physical Society, 2017)
    A general formalism is introduced to allow the steady state of non-Markovian processes on networks to be reduced to equivalent Markovian processes on the same substrates. The example of an epidemic spreading process is ...
  • 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 ...
  • Hurd, Thomas R; Gleeson, James P.; Melnik, Sergey (Public Library of Science, 2017)
    We introduce a probabilistic framework that represents stylized banking networks with the aim of predicting the size of contagion events. Most previous work on random financial networks assumes independent connections ...
  • 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 ...
  • O'Sullivan, David J.P.; Garduño-Hernández, Guillermo; Gleeson, James P.; Beguerisse-Díaz, Mariano (The Royal Society, 2017)
    We examine the relationship between social structure and sentiment through the analysis of a large collection of tweets about the Irish Marriage Referendum of 2015. We obtain the sentiment of every tweet with the hashtags ...
  • Goulding, D.; Melnik, Sergey; Curtin, D.; Piwonski, T.; Houlihan, J.; Gleeson, James P.; Hayet, G. (American Physical Society, 2007)
    no abstract available
  • Fennell, Peter G.; Melnik, Sergey; Gleeson, James P. (American Physical Society, 2016)
    Continuous-time Markov process models of contagions are widely studied, not least because of their utility in predicting the evolution of real-world contagions and in formulating control measures. It is often the case, ...
  • O'Sullivan, David J.P.; O'Keeffe, Gary J.; Fennell, Peter G.; Gleeson, James P. (Frontiers Media, 2015)
    The spreading of behavior, such as the adoption of a new innovation, is influenced by the structure of social networks that interconnect the population. In the experiments of Centola [15], adoption of new behavior was shown ...
  • 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 ...
  • Fennell, Peter G.; Gleeson, James P. (Society for Industrial and Applied Mathematics, 2019)
    Multistate dynamical processes on networks, where nodes can occupy one of a multitude of discrete states, are gaining widespread use because of their ability to recreate realistic, complex behavior that cannot be adequately ...
  • 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 ...
  • Faqeeh, Ali (University of Limerick, 2016)
    Many of the systems we observe in nature, in societies, or in infrastructures are in the form of a network of interacting units. This underlying network structure shapes the behavior of such systems and is an indispensable ...
  • 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 ...
  • Hurley, Julie (University of Limerick, 2011)
    One of the most controversial and innovative finnancial products in recent years has been collateralised debt obligations (CDOs). Much of the blame for the current credit crisis is being attributed to the mathematical models ...
  • Devine, Mel T. (University of Limerick, 2012)
    In recent years the daily gas demand in the UK and Ireland has become increasingly uncertain. This due to the changing nature of electricity markets, where intermittent wind energy levels lead to variations in the demand ...
  • Kartun-Giles, Alexander P.; Krioukov, Dmitri; Gleeson, James P.; Moreno, Yamir; Bianconi, Ginestra (MDPI, 2018)
    A projective network model is a model that enables predictions to be made based on a subsample of the network data, with the predictions remaining unchanged if a larger sample is taken into consideration. An exchangeable ...
  • O'Brien, Joseph D.; Dassios, Ioannis K.; Gleeson, James P. (IOP Publishing, 2019)
    A model for the spreading of online information or ‘memes’ on multiplex networks is introduced and analyzed using branching-process methods. The model generalizes that of (Gleeson et al 2016 Phys. Rev.X) in two ways. ...

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