Percolation in real interdependent networks

TL;DR

This paper introduces a new theoretical method to analyze percolation in finite, real interdependent networks by decomposing them into intersections and remainders, revealing how their structure influences the nature of phase transitions.

Contribution

The authors develop a framework that predicts percolation phase diagrams in finite interdependent networks directly from adjacency matrices, without simulations, accounting for real-world network complexities.

Findings

Percolation transitions depend on the dominance of intersection or remainders.

Networks with a dominant intersection exhibit continuous transitions.

Networks dominated by remainders show abrupt, catastrophic failures.

Abstract

The function of a real network depends not only on the reliability of its own components, but is affected also by the simultaneous operation of other real networks coupled with it. Robustness of systems composed of interdependent network layers has been extensively studied in recent years. However, the theoretical frameworks developed so far apply only to special models in the limit of infinite sizes. These methods are therefore of little help in practical contexts, given that real interconnected networks have finite size and their structures are generally not compatible with those of graph toy models. Here, we introduce a theoretical method that takes as inputs the adjacency matrices of the layers to draw the entire phase diagram for the interconnected network, without the need of actually simulating any percolation process. We demonstrate that percolation transitions in arbitrary interdependent networks can be understood by decomposing these system into uncoupled graphs: the intersection among the layers, and the remainders of the layers. When the intersection dominates the remainders, an interconnected network undergoes a continuous percolation transition. Conversely, if the intersection is dominated by the contribution of the remainders, the transition becomes abrupt even in systems of finite size. We provide examples of real systems that have developed interdependent networks sharing a core of "high quality" edges to prevent catastrophic failures.