QRE: Quick Robustness Estimation for large complex networks

Sebastian Wandelt, Xiaoqian Sun, Massimiliano Zanin, Shlomo Havlin

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Robustness estimation is critical for the design and maintenance of resilient networks. Existing studies on network robustness usually exploit a single network metric to generate attack strategies, which simulate intentional attacks on a network, and compute a metric-induced robustness estimation, called R. While some metrics are easy to compute, e.g. degree, others require considerable computation efforts, e.g. betweenness centrality. We propose Quick Robustness Estimation (QRE), a new framework and implementation for estimating the robustness of a network in sub-quadratic time, i.e., significantly faster than betweenness centrality, based on the combination of cheap-to-compute network metrics. Experiments on twelve real-world networks show that QRE estimates the robustness better than betweenness centrality-based computation, while being at least one order of magnitude faster for larger networks. Our work contributes towards scalable, yet accurate robustness estimation for large complex networks.

Original languageEnglish
Pages (from-to)413-424
Number of pages12
JournalFuture Generation Computer Systems - The International Journal of Grid Computing and eScience
Publication statusPublished - 1 Jun 2018


  • Complex networks
  • Robustness estimation
  • Scalability


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