Percolation and epidemic thresholds in clustered networks
Percolation and epidemic thresholds in clustered networks
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DOI：
10.1103/physrevlett.97.088701
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发表时间：
2006-08-25
影响因子：
8.6
通讯作者：
Boguna, Marian
中科院分区：
文献类型：
--
作者：
Serrano, M. Angeles;Boguna, Marian
我们开发了一种随机集群网络中渗透的理论方法。我们发现，尽管无标度网络中的聚类可以强烈影响某些渗流特性，例如巨型连通分量的大小和弹性，但它无法恢复有限的渗流阈值。反过来，这意味着此类网络不存在流行阈值，从而将此结果扩展到各种真实的无标度网络，显示出高水平的传递性。我们的研究结果与数值模拟非常吻合。
We develop a theoretical approach to percolation in random clustered networks. We find that, although clustering in scale-free networks can strongly affect some percolation properties, such as the size and the resilience of the giant connected component, it cannot restore a finite percolation threshold. In turn, this implies the absence of an epidemic threshold in this class of networks, thus extending this result to a wide variety of real scale-free networks which shows a high level of transitivity. Our findings are in good agreement with numerical simulations.