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arXiv 1407.4744math.PRcs.SIphysics.soc-ph
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扩散网络中影响的紧界及其在键渗流和流行病学中的应用

Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology

Remi Lemonnier, Kevin Scaman, Nicolas Vayatis

摘要:

在本文中,我们推导出一个独立级联模型(ICM)中的一个节点的长期影响的理论界。我们将这些界限与一个特定矩阵的谱半径相关联,并表明当该谱半径低于$1$时,该行为是次临界的。更具体地说,我们指出,在一般的网络,次临界制度的行为在$O(\sqrt{n})$其中$n$是网络的大小,这上界满足星形网络。我们将我们的研究结果应用于任意网络的流行病学和渗流,并推导出一个巨大的连接组件出现的临界值的界限。最后,我们经验性地展示了一个大的网络家族的界限的紧密性。

英文摘要:

In this paper, we derive theoretical bounds for the long-term influence of a node in an Independent Cascade Model (ICM). We relate these bounds to the spectral radius of a particular matrix and show that the behavior is sub-critical when this spectral radius is lower than $1$. More specifically, we point out that, in general networks, the sub-critical regime behaves in $O(\sqrt{n})$ where $n$ is the size of the network, and that this upper bound is met for star-shaped networks. We apply our results to epidemiology and percolation on arbitrary networks, and derive a bound for the critical value beyond which a giant connected component arises. Finally, we show empirically the tightness of our bounds for a large family of networks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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