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

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

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  • 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 ...
  • 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 ...
  • Cellai, Davide; Dorogovtsev, Sergey N.; Bianconi, Ginestra (Ameriican Physical Society, 2016)
    Multiplex networks describe a large variety of complex systems, including infrastructures, transportation networks, and biological systems. Most of these networks feature a significant link overlap. It is therefore of ...
  • Cellai, Davide; Bianconi, Ginestra (American Physical Society, 2016)
    In multiplex networks with a large number of layers, the nodes can have different activities, indicating the total number of layers in which the nodes are present. Here we model multiplex networks with heterogeneous activity ...
  • 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 ...
  • Baxter, Gareth J.; Dorogovtsev, Sergey N.; Mendes, José F.F.; Cellai, Davide (Americann Physical Society, 2014)
    Bootstrap percolation is a simple but nontrivial model. It has applications in many areas of science and has been explored on random networks for several decades. In single-layer (simplex) networks, it has been recently ...

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