Comprehensive comparison and accuracy of graph metrics in predicting network resilience

Abstract

Graph robustness metrics have been used largely to study the behavior of communication networks in the presence of targeted attacks and random failures. Several researchers have proposed new graph metrics to better predict network resilience and survivability against such attacks. Most of these metrics have been compared to a few established graph metrics for evaluating the effectiveness of measuring network resilience. In this paper, we perform a comprehensive comparison of the most commonly used graph robustness metrics. First, we show how each metric is determined and calculate its values for baseline graphs. Using several types of random graphs, we study the accuracy of each robustness metric in predicting network resilience against centrality-based attacks. The results show three conclusions. First, our path diversity metric has the highest accuracy in predicting network resilience for structured baseline graphs. Second, the variance of node-betweenness centrality has mostly the best accuracy in predicting network resilience for Waxman random graphs. Third, path diversity, network criticality, and effective graph resistance have high accuracy in measuring network resilience for Gabriel graphs.

A Temporal–Spatial Model-Based Method of Resilience Measurement for Mobile Ad Hoc Network

“…The network generally has higher robustness and resilience levels when α = 0.5 and β = 0.8, and this result is consistent with the result in Ref. 10. Under the same physical layer setup, the values of TSFR and STSFR are lower than those of phyFR and SphyFR, because the temporal-spatial abstract layer is above the physical layer, and an AODV routing protocol cannot use all physical connections but chooses some of them as routes.…”

“…It is noteworthy that phyFR is the metric mentioned in Ref. 10 that is more accurate than other structural-based metrics for resilience evaluation. From Fig.…”

“…(6,13,17) Although all of these are capable of measuring the robustness of the network structure, none completely reflects the impact of the routing mechanism on the network directly. Recently, scholars have begun to consider the impact of packet transmission and proposed flow robustness (14) metrics to measure the flow robustness (phyFR) of the physical layer as an assessment of the resilience of a network, (10) and compared flow robustness with other structure-based metrics. This method showed a higher accuracy than others:…”

“…Measure the resilience of the network: Referring to the method of calculating resilience from Alenazi et al, (10) we used the sum of the temporal-spatial flow robustness (STSFR) to denote the resilience: STSFR n = …”

“…To deal with these problems, (9) over the past few years, a plethora of structure-based approaches and metrics have been proposed to measure network resilience. (5,(9)(10)(11) The common structure-based methods depend on the robustness evaluation of structural topology against attacks and failures. In the network science area, a series of metrics based on complex network robustness and corresponding methods have been proposed: Sydney et al (12) proposed a network resilience assessment method based on algebraic connectivity; Alenazi et al (9) introduced a method to evaluate network resilience based on spectral measure metrics, which is much more accurate than connectivity metrics for robustness assessment.…”