Comprehensive comparison and accuracy of graph metrics in predicting network resilience

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.

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