The combined effect of connectivity and dependency links on percolation of networks

Percolation theory is extensively studied in statistical physics and mathematics with applications in diverse fields. However, the research is focused on systems with only one type of links, connectivity links. We review a recently developed mathematical framework for analyzing percolation properties of realistic scenarios of networks having links of two types, connectivity and dependency links. This formalism was applied to study Erd$\ddot{o}$s-R$\acute{e}$nyi (ER) networks that include also dependency links. For an ER network with average degree $k$ that is composed of dependency clusters of size $s$, the fraction of nodes that belong to the giant component, $P_\infty$, is given by $P_\infty=p^{s-1}[1-\exp{(-kpP_\infty)}]^s$ where $1-p$ is the initial fraction of randomly removed nodes. Here, we apply the formalism to the study of random-regular (RR) networks and find a formula for the size of the giant component in the percolation process: $P_\infty=p^{s-1}(1-r^k)^s$ where $r$ is the solution of $r=p^s(r^{k-1}-1)(1-r^k)+1$. These general results coincide, for $s=1$, with the known equations for percolation in ER and RR networks respectively without dependency links. In contrast to $s=1$, where the percolation transition is second order, for $s>1$ it is of first order. Comparing the percolation behavior of ER and RR networks we find a remarkable difference regarding their resilience. We show, analytically and numerically, that in ER networks with low connectivity degree or large dependency clusters, removal of even a finite number (zero fraction) of the network nodes will trigger a cascade of failures that fragments the whole network. This result is in contrast to RR networks where such cascades and full fragmentation can be triggered only by removal of a finite fraction of nodes in the network.Comment: 11 pages, 3 figure

Universality of the Directed Polymer Model

The universality of the directed polymer model and the analogous KPZ equation is supported by numerical simulations using non-Gaussian random probability distributions in two, three and four dimensions. It is shown that although in the non-Gaussian cases the \emph{finite size} estimates of the energy exponents are below the persumed universal values, these estimates \emph{increase} with the system size, and the further they are below the universal values, the higher is their rate of increase. The results are explained in terms of the efficiency of variance reduction during the optimization process.Comment: RevTeX, 3 postscript figure

Optimal Path in Two and Three Dimensions

We apply the Dijkstra algorithm to generate optimal paths between two given sites on a lattice representing a disordered energy landscape. We study the geometrical and energetic scaling properties of the optimal path where the energies are taken from a uniform distribution. Our numerical results for both two and three dimensions suggest that the optimal path for random uniformly distributed energies is in the same universality class as the directed polymers. We present physical realizations of polymers in disordered energy landscape for which this result is relevant.Comment: 7 pages, 4 figure

The critical effect of dependency groups on the function of networks

Current network models assume one type of links to define the relations between the network entities. However, many real networks can only be correctly described using two different types of relations. Connectivity links that enable the nodes to function cooperatively as a network and dependency links that bind the failure of one network element to the failure of other network elements. Here we present for the first time an analytical framework for studying the robustness of networks that include both connectivity and dependency links. We show that the synergy between the two types of failures leads to an iterative process of cascading failures that has a devastating effect on the network stability and completely alters the known assumptions regarding the robustness of networks. We present exact analytical results for the dramatic change in the network behavior when introducing dependency links. For a high density of dependency links the network disintegrates in a form of a first order phase transition while for a low density of dependency links the network disintegrates in a second order transition. Moreover, opposed to networks containing only connectivity links where a broader degree distribution results in a more robust network, when both types of links are present a broad degree distribution leads to higher vulnerability.Comment: 5 pages, 4 figure