Effect of the interconnected network structure on the epidemic threshold
Abstract
Most realworld networks are not isolated. In order to function fully, they are interconnected with other networks, and this interconnection influences their dynamic processes. For example, when the spread of a disease involves two species, the dynamics of the spread within each species (the contact network) differs from that of the spread between the two species (the interconnected network). We model two generic interconnected networks using two adjacency matrices, A and B, in which A is a 2N×2N matrix that depicts the connectivity within each of two networks of size N, and B a 2N×2N matrix that depicts the interconnections between the two. Using an Nintertwined meanfield approximation, we determine that a critical susceptibleinfectedsusceptible (SIS) epidemic threshold in two interconnected networks is 1/λ_{1}(A+αB), where the infection rate is β within each of the two individual networks and αβ in the interconnected links between the two networks and λ_{1}(A+αB) is the largest eigenvalue of the matrix A+αB. In order to determine how the epidemic threshold is dependent upon the structure of interconnected networks, we analytically derive λ_{1}(A+αB) using a perturbation approximation for small and large α, the lower and upper bound for any α as a function of the adjacency matrix of the two individual networks, and the interconnections between the two and their largest eigenvalues and eigenvectors. We verify these approximation and boundary values for λ_{1}(A+αB) using numerical simulations, and determine how component network features affect λ_{1}(A+αB). We note that, given two isolated networks G_{1} and G_{2} with principal eigenvectors x and y, respectively, λ_{1}(A+αB) tends to be higher when nodes i and j with a higher eigenvector component product x_{i}y_{j} are interconnected. This finding suggests essential insights into ways of designing interconnected networks to be robust against epidemics.
 Publication:

Physical Review E
 Pub Date:
 August 2013
 DOI:
 10.1103/PhysRevE.88.022801
 arXiv:
 arXiv:1303.0781
 Bibcode:
 2013PhRvE..88b2801W
 Keywords:

 89.75.k;
 87.23.Ge;
 89.20.a;
 Complex systems;
 Dynamics of social systems;
 Interdisciplinary applications of physics;
 Physics  Physics and Society
 EPrint:
 doi:10.1103/PhysRevE.88.022801