Abstract
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.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61672298, 61373136, 61374180), the Ministry of Education Research in the Humanities and Social Sciences Planning Fund of China (Grant Nos. 17YJAZH071, 15YJAZH016).
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Li, YW., Zhang, ZH., Fan, D., Song, YR., Jiang, GP. (2018). Influence of Clustering on Network Robustness Against Epidemic Propagation. In: Liu, F., Xu, S., Yung, M. (eds) Science of Cyber Security. SciSec 2018. Lecture Notes in Computer Science(), vol 11287. Springer, Cham. https://doi.org/10.1007/978-3-030-03026-1_2
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