ELASTICITY: Topological Characterization of Robustness in Complex Networks

TL;DR

This paper introduces elasticity, a new metric for assessing the robustness of complex networks, balancing model accuracy and simplicity, and evaluates it on Internet and social network topologies.

Contribution

The paper proposes elasticity as a novel, practical robustness metric that bridges the gap between complex accuracy and simple applicability in network analysis.

Findings

Elasticity effectively measures network robustness.

Elasticity performs well on Internet topologies.

Elasticity provides insights into social network resilience.

Abstract

Just as a herd of animals relies on its robust social structure to survive in the wild, similarly robustness is a crucial characteristic for the survival of a complex network under attack. The capacity to measure robustness in complex networks defines the resolve of a network to maintain functionality in the advent of classical component failures and at the onset of cryptic malicious attacks. To date, robustness metrics are deficient and unfortunately the following dilemmas exist: accurate models necessitate complex analysis while conversely, simple models lack applicability to our definition of robustness. In this paper, we define robustness and present a novel metric, elasticity- a bridge between accuracy and complexity-a link in the chain of network robustness. Additionally, we explore the performance of elasticity on Internet topologies and online social networks, and articulate results.