Influence of Clustering on Network Robustness Against Epidemic Propagation.

How clustering affects network robustness against epidemic propagation is investigated in this paper. The epidemic threshold, the fraction of infected nodes at steady state and epidemic velocity are adopted as the network robustness index. With the help of the networks generated by the 1K null model algorithm (with identical degree distribution), we use three network propagation models (SIS, SIR, and SI) to investigate the influence of clustering against epidemic propagation. The results of simulation show that the clustering of heterogeneous networks has little influence on the network robustness. In homogeneous networks, there is limited increase in epidemic threshold by increasing clustering. However, the fraction of infected nodes at steady state and epidemic velocity evidently decrease with the increase of clustering. By virtue of the generated null models, we further study the relationship between clustering and global efficiency. We find that the global efficiency of networks decreases monotonically with the increase of clustering. This result suggests that we can decrease the epidemic velocity by increasing network clustering.