University of Limerick Institutional Repository

Browsing by Author "Hackett, Adam W."

DSpace Repository

Browsing by Author "Hackett, Adam W."

Sort by: Order: Results:

  • Hackett, Adam W.; Cellai, Davide; Gomez, S.; Arenas, A.; Gleeson, James P. (American Physical Society, 2016)
    We present an analytical approach for bond percolation on multiplex networks and use it to determine the expected size of the giant connected component and the value of the critical bond occupation probability in these ...
  • 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.; 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 ...
  • 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 ...