Article

Structural transition in interdependent networks with regular interconnections

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Abstract

Networks are often made up of several layers that exhibit diverse degrees of interdependencies. An interdependent network consists of a set of graphs G that are interconnected through a weighted interconnection matrix B, where the weight of each intergraph link is a non-negative real number p. Various dynamical processes, such as synchronization, cascading failures in power grids, and diffusion processes, are described by the Laplacian matrix Q characterizing the whole system. For the case in which the multilayer graph is a multiplex, where the number of nodes in each layer is the same and the interconnection matrix B=pI, I being the identity matrix, it has been shown that there exists a structural transition at some critical coupling p*. This transition is such that dynamical processes are separated into two regimes: if p>p*, the network acts as a whole; whereas when p<p*, the network operates as if the graphs encoding the layers were isolated. In this paper, we extend and generalize the structural transition threshold p* to a regular interconnection matrix B (constant row and column sum). Specifically, we provide upper and lower bounds for the transition threshold p* in interdependent networks with a regular interconnection matrix B and derive the exact transition threshold for special scenarios using the formalism of quotient graphs. Additionally, we discuss the physical meaning of the transition threshold p* in terms of the minimum cut and show, through a counterexample, that the structural transition does not always exist. Our results are one step forward on the characterization of more realistic multilayer networks and might be relevant for systems that deviate from the topological constraints imposed by multiplex networks.

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... The eigen-pair of eigenvalue 2p and eigenvector (u, −u) holds for directed multilayer networks, which is previously found in undirected multilayered networks [12,21]. The joint effect of the Laplacian matrices Q 1 and Q 2 for each layer, encoded in the matrix ΔQ, determines the deviation of the convergence rate of the whole system from that of the integrated multilayer. ...
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... The eigen-pair of eigenvalue 2p and eigenvector (u, −u) holds for directed multilayer networks, which is previously found in undirected multilayered networks [11,21]. The joint effect of the Laplacian matrices Q 1 and Q 2 for each layer, encoded in the matrix ∆Q, determines the deviation of the convergence rate of the whole system from that of the integrated multilayer. ...
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  • F. Harary
Robustness of interdependent networks: The case of communication networks and the power grid
  • M Parandehgheibi
  • E Modiano
M. Parandehgheibi and E. Modiano, Robustness of interdependent networks: The case of communication networks and the power grid, in 2013 IEEE Global Communications Conference (GLOBECOM) (IEEE, Atlanta, 2013), pp. 2164-2169.