Interdependent networks with correlated degrees of mutually dependent nodes

We study a problem of failure of two interdependent networks in the case of correlated degrees of mutually dependent nodes. We assume that both networks (A and B) have the same number of nodes $N$ connected by the bidirectional dependency links establishing a one-to-one correspondence between the nodes of the two networks in a such a way that the mutually dependent nodes have the same number of connectivity links, i.e. their degrees coincide. This implies that both networks have the same degree distribution $P(k)$. We call such networks correspondently coupled networks (CCN). We assume that the nodes in each network are randomly connected. We define the mutually connected clusters and the mutual giant component as in earlier works on randomly coupled interdependent networks and assume that only the nodes which belong to the mutual giant component remain functional. We assume that initially a $1-p$ fraction of nodes are randomly removed due to an attack or failure and find analytically, for an arbitrary $P(k)$, the fraction of nodes $\mu(p)$ which belong to the mutual giant component. We find that the system undergoes a percolation transition at certain fraction $p=p_c$ which is always smaller than the $p_c$ for randomly coupled networks with the same $P(k)$. We also find that the system undergoes a first order transition at $p_c>0$ if $P(k)$ has a finite second moment. For the case of scale free networks with $2<\lambda \leq 3$, the transition becomes a second order transition. Moreover, if $\lambda<3$ we find $p_c=0$ as in percolation of a single network. For $\lambda=3$ we find an exact analytical expression for $p_c>0$. Finally, we find that the robustness of CCN increases with the broadness of their degree distribution.Comment: 18 pages, 3 figure

Inter-similarity between coupled networks

Recent studies have shown that a system composed from several randomly interdependent networks is extremely vulnerable to random failure. However, real interdependent networks are usually not randomly interdependent, rather a pair of dependent nodes are coupled according to some regularity which we coin inter-similarity. For example, we study a system composed from an interdependent world wide port network and a world wide airport network and show that well connected ports tend to couple with well connected airports. We introduce two quantities for measuring the level of inter-similarity between networks (i) Inter degree-degree correlation (IDDC) (ii) Inter-clustering coefficient (ICC). We then show both by simulation models and by analyzing the port-airport system that as the networks become more inter-similar the system becomes significantly more robust to random failure.Comment: 4 pages, 3 figure