Epidemic and Cascading Survivability of Complex Networks

TL;DR

This paper introduces two novel measures, epidemic survivability and cascading survivability, to assess the vulnerability of complex networks under epidemic-like and cascading failure scenarios, providing insights into network robustness.

Contribution

The paper proposes new metrics for evaluating network vulnerability under different failure scenarios, enhancing understanding of complex network resilience.

Findings

Different network types respond uniquely to failure scenarios.

The proposed measures effectively characterize node vulnerability.

Distribution of ES and CS values describes overall network vulnerability.

Abstract

Our society nowadays is governed by complex networks, examples being the power grids, telecommunication networks, biological networks, and social networks. It has become of paramount importance to understand and characterize the dynamic events (e.g. failures) that might happen in these complex networks. For this reason, in this paper, we propose two measures to evaluate the vulnerability of complex networks in two different dynamic multiple failure scenarios: epidemic-like and cascading failures. Firstly, we present \emph{epidemic survivability} ($ES$), a new network measure that describes the vulnerability of each node of a network under a specific epidemic intensity. Secondly, we propose \emph{cascading survivability} ($CS$), which characterizes how potentially injurious a node is according to a cascading failure scenario. Then, we show that by using the distribution of values obtained from $ES$ and $CS$ it is possible to describe the vulnerability of a given network. We consider a set of 17 different complex networks to illustrate the suitability of our proposals. Lastly, results reveal that distinct types of complex networks might react differently under the same multiple failure scenario.