Robustness of Interdependent Random Geometric Networks

TL;DR

This paper models interdependent spatial networks using random geometric graphs to analyze their robustness, revealing complex percolation thresholds and conditions for resilience against failures and attacks.

Contribution

It introduces an interdependent RGG model, derives analytical bounds on percolation thresholds, and explores robustness conditions for spatially embedded networks.

Findings

Multiple percolation thresholds can exist for interdependent RGGs.

Analytical upper bounds on thresholds are derived via discretization.

Conditions for network robustness under failures and attacks are established.

Abstract

We propose an interdependent random geometric graph (RGG) model for interdependent networks. Based on this model, we study the robustness of two interdependent spatially embedded networks where interdependence exists between geographically nearby nodes in the two networks. We study the emergence of the giant mutual component in two interdependent RGGs as node densities increase, and define the percolation threshold as a pair of node densities above which the giant mutual component first appears. In contrast to the case for a single RGG, where the percolation threshold is a unique scalar for a given connection distance, for two interdependent RGGs, multiple pairs of percolation thresholds may exist, given that a smaller node density in one RGG may increase the minimum node density in the other RGG in order for a giant mutual component to exist. We derive analytical upper bounds on the percolation thresholds of two interdependent RGGs by discretization, and obtain $99\%$ confidence intervals for the percolation thresholds by simulation. Based on these results, we derive conditions for the interdependent RGGs to be robust under random failures and geographical attacks.