A Novel Metric to Quantify the Real-Time Robustness of Complex Networks With Respect to Epidemic Models - Song, Bo; Jiang, Guo-Ping; Song, Yurong; Yang, Junming; Wang, Xu; Guo, Y. Jay (Frontiers in Physics, 2022)
Spread velocity, epidemic threshold and infection density at steady state are three non-negligible features to describe the epidemic spread. Combining the three features together, a new network robustness metric with respect to epidemics is proposed in this paper. The real-time robustness of the network is defined and analyzed. By using the SI and SIS epidemic models, the robustness of different networks is analyzed based on the proposed network robustness metric. Simulation results show that homogeneous networks present stronger robustness than heterogeneous networks at the early stage of the epidemics, and the robustness of heterogeneous network becomes stronger than that of homogeneous ones with the progress of the epidemic. Moreover, the irregularity of degree distribution decreases the network robustness in homogeneous networks. The network becomes more vulnerable as the average degree grows in both homogeneous and heterogeneous networks.