Optimal paths in complex networks with correlated weights: The worldwide airport network

We study complex networks with weights w(ij) associated with each link connecting node i and j. The weights are chosen to be correlated with the network topology in the form found in two real world examples: (a) the worldwide airport network and (b) the E. Coli metabolic network. Here w(ij)similar to x(ij)(k(i)k(j))(alpha), where k(i) and k(j) are the degrees of nodes i and j, x(ij) is a random number, and alpha represents the strength of the correlations. The case alpha>0 represents correlation between weights and degree, while alpha < 0 represents anticorrelation and the case alpha=0 reduces to the case of no correlations. We study the scaling of the lengths of the optimal paths, center dot(opt), with the system size N in strong disorder for scale-free networks for different alpha. We find two different universality classes for center dot(opt) in strong disorder depending on alpha: (i) if alpha>0, then for lambda>2 the scaling law center dot(opt)similar to N-1/3, where lambda is the power-law exponent of the degree distribution of scale-free networks, and (ii) if alpha <= 0, then center dot(opt)similar to N-opt(nu) with nu(opt) identical to its value for the uncorrelated case alpha=0. We calculate the robustness of correlated scale-free networks with different alpha and find the networks with alpha < 0 to be the most robust networks when compared to the other values of alpha. We propose an analytical method to study percolation phenomena on networks with this kind of correlation, and our numerical results suggest that for scale-free networks with alpha < 0, the percolation threshold p(c) is finite for lambda>3, which belongs to the same universality class as alpha=0. We compare our simulation results with the real worldwide airport network, and we find good agreement.