Cavity analysis on the robustness of random networks against targeted attacks: Influences of degree-degree correlations

TL;DR

This paper introduces a cavity method-based scheme to evaluate the robustness of random networks with degree correlations against targeted attacks, revealing that degree correlations significantly influence network resilience.

Contribution

The study develops a novel cavity method approach to analyze the impact of degree correlations on network robustness under targeted attacks.

Findings

Degree correlations affect network robustness non-trivially.

Bimodal networks' resilience varies with degree correlation patterns.

Targeted attacks' impact depends on network configuration details.

Abstract

We developed a scheme for evaluating the size of the largest connected subnetwork (giant component) in random networks and the percolation threshold when sites (nodes) and/or bonds (edges) are removed from the networks based on the cavity method of statistical mechanics of disordered systems. An advantage of our scheme is the capability of handling targeted attacks on sites/bonds in the presence of degree correlations beyond naive analyses on random failures (crashes) in networks of no degree correlations. We apply our scheme particularly to random networks of bimodal degree distribution (two-peak networks), which have been proposed in earlier studies as robust networks against random failures of site and/or targeted attacks on sites, and show that the correlations among degrees affect a network's robustness against targeted attacks on sites or bonds non-trivially depending on details of network configurations.