Robustness of Interdependent Random Geometric Networks

作     者：Zhang, Jianan Yeh, Edmund Modiano, Eytan 

作者机构：Laboratory for Information and Decision Systems Massachusetts Institute of Technology Electrical and Computer Engineering Department Northeastern University 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2017年

核心收录：

主　　题：Solvents 

摘      要：—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. Copyright © 2017, The Authors. All rights reserved.