Measuring Network Robustness by Average Network Flow

TL;DR

This paper introduces Average Network Flow (ANF) as a new robustness metric for infrastructure networks, demonstrating its properties, efficient computation, and comparing it with existing metrics to provide a comprehensive robustness assessment.

Contribution

The paper proposes ANF as a global, strictly increasing robustness metric with quadratic complexity, and provides an efficient algorithm using Gomory-Hu trees, filling a gap in existing metrics.

Findings

ANF increases strictly with link additions

ANF can be computed with quadratic complexity using Gomory-Hu trees

Different robustness metrics exhibit unique characteristics

Abstract

Infrastructure networks such as the Internet backbone and power grids are essential for our everyday lives. With the prevalence of cyber-attacks on them, measuring their robustness has become an important issue. To date, many robustness metrics have been proposed. It is desirable for a robustness metric to possess the following three properties: considering global network topologies, strictly increasing upon link additions, and having a quadratic complexity in terms of the number of nodes on sparse networks. This paper proposes to use Average Network Flow (ANF) as a robustness metric, and proves that it increases strictly, and gives an algorithm to compute ANF with a quadratic complexity by leveraging Gomory-Hu trees. Thus, with ANF intrinsically considering global network topologies, ANF is unveiled to be a new robustness metric satisfying those three properties. Moreover, this paper compares ANF with seven existing representative metrics, showing that each metric has its own characteristics, so there is no silver bullet in measuring network robustness and it is recommended to apply several metrics together to gain a comprehensive view. Finally, by experimenting on the scenarios in which network topologies preserve the same numbers of nodes and links, some interesting behaviors of robustness metrics are reported.