Cascading failures in isotropic and anisotropic spatial networks induced by localized attacks and overloads

Abstract

Cascading failures are catastrophic processes that can destroy the functionality of a system, thus,understanding their development in real infrastructures is of vital importance. This may lead to abetter management of everyday complex infrastructures relevant to modern societies, e.g., electrical power grids, communication and traffic networks. In this paper we examine the Motter–Lai model (2002 Phys. Rev. E 66 065102) of cascading failures induced by overloads in both isotropic and anisotropic spatial networks, generated by placing nodes in a square lattice and using various distributions of link lengths and angles. Anisotropy has not been earlier considered in the Motter–Lai model and is a real feature that may affect the cascading failures. This could reflect the existence of a preferred direction in which a given attribute of the system manifests, such as power lines that follow a city built parallel to the coast. We analyze the evolution of the cascading failures for systems with different strengths of anisotropy and show that the anisotropy causes a greater spread of damage along the preferential direction of links. We identify the critical linear size, lc , for a square shaped localized attack, which satisfies with high probability that above l c the cascading disrupts the giant component of functional nodes, while below lc the damage does not spread. We find that, for networks with any characteristic link length, their robustness decreases with the strength of the anisotropy. We show that the value of lc is finite and independent of the system size (for large systems), both for isotropic and anisotropic networks. Thus, in contrast to random attacks, where the critical fraction of nodes that survive the initial attack, pc , is usually below 1, here pc = 1. Note that the analogy to pc = 1 is also found for localized attacks in interdependent spatial networks (Berezin et al 2015 Sci. Rep. 5 8934). Finally, we measure the final distribution of functional cluster sizes and find a power-law behavior, with exponents similar to regular percolation. This indicates that, after the cascade which destroys the giant component, the system is at a percolation critical point. Additionally, we observe a crossover in the value of the distribution exponent, from critical percolation in a two-dimensional lattice for strong spatial embedding, to mean-field percolation for weak embedding.