Breakdown of interdependent directed networks

TLDR

Real‑world complex systems interact, raising the risk of catastrophic failure, and interdependent network models help assess robustness, yet prior work has focused on undirected layers while most real networks are directed with in‑ and out‑degree correlations. We aim to develop a general theoretical framework for analyzing the breakdown of interdependent directed networks with or without in‑degree and out‑degree correlations, applying it to international trade networks. The framework models interdependent directed networks, incorporating in‑ and out‑degree correlations, and is applied to real‑world international trade data. We find that in‑ and out‑degree correlations increase the robustness of interdependent heterogeneous networks but decrease the robustness of interdependent homogeneous networks with strong coupling strengths.

Abstract

Significance Real-world complex systems interact with one another, and these interactions increase the probability of catastrophic failure. Using interdependent networks to model these phenomena helps understand a system’s robustness and enables design of more robust infrastructures. Previous research has been limited to an idealized case where each layer is undirected, but almost all real-world networks are directed and exhibit in-degree and out-degree correlations. Therefore, we develop a general theoretical framework for analyzing the breakdown of interdependent directed networks with, or without, in-degree and out-degree correlations, and apply it to real-world international trade networks. Surprisingly, we find that the robustness of interdependent heterogeneous networks increases, whereas that of interdependent homogeneous networks with strong coupling strengths decreases with in-degree and out-degree correlations.