Existing link attack strategies in networks differ in the importance or robustness metric, that quantifies the effect of a link removal upon the network’s vulnerability. In this paper, we investigate the role of the effective resistance matrix in the removal of links on a graph and compare this removal strategy with other state-of-the-art attack strategies over synthetic networks. The results of the analysis show that the effective resistance and the link-betweenness strategies behave similarly and are more harmful than the degree based strategies when evaluating robustness with different performance measures.

Initially emerged in the Chinese city Wuhan and subsequently spread almost worldwide causing a pandemic, the SARS-CoV-2 virus follows reasonably well the Susceptible–Infectious–Recovered (SIR) epidemic model on contact networks in the Chinese case. In this paper, we investigate the prediction accuracy of the SIR model on networks also for Italy. Specifically, the Italian regions are a metapopulation represented by network nodes and the network links are the interactions between those regions. Then, we modify the network-based SIR model in order to take into account the different lockdown measures adopted by the Italian Government in the various phases of the spreading of the COVID-19. Our results indicate that the network-based model better predicts the daily cumulative infected individuals when time-varying lockdown protocols are incorporated in the classical SIR model.