Robustness of partially interdependent network formed of clustered networks

TL;DR

This paper investigates how clustering within networks influences the robustness and percolation properties of partially interdependent networks, extending previous work from fully coupled pairs to more general network configurations.

Contribution

It generalizes the analysis of clustering effects on interdependent networks from fully coupled pairs to partially interdependent networks with clustering within components.

Findings

Clustering affects percolation thresholds and giant component size.

Clustering can change the nature of phase transitions in network robustness.

Different clustering models influence network resilience differently.

Abstract

Clustering, or transitivity has been observed in real networks and its effects on their structure and function has been discussed extensively. The focus of these studies has been on clustering of single networks while the effect of clustering on the robustness of coupled networks received very little attention. Only the case of a pair of fully coupled networks with clustering has been studied recently. Here we generalize the study of clustering of a fully coupled pair of networks to the study of partially interdependent network of networks with clustering within the network components. We show both analytically and numerically, how clustering within the networks, affects the percolation properties of interdependent networks, including percolation threshold, size of giant component and critical coupling point where first order phase transition changes to second order phase transition as the coupling between the networks reduces. We study two types of clustering: one type proposed by Newman where the average degree is kept constant while changing the clustering and the other proposed by Hackett $et$ $al.$ where the degree distribution is kept constant. The first type of clustering is treated both analytically and numerically while the second one is treated only numerically.