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arXiv 1506.01255math.PRmath.CO
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方差度为无穷大的随机图的首次通过渗流

First passage percolation on random graphs with infinite variance degrees

Enrico Baroni, Remco van der Hofstad, Julia Komjathy

摘要:

我们证明了在具有独立同分布的组态模型上首次通过渗流的非普适性结果。具有无穷大方差的度。我们专注于两个一致的顶点之间的最优路径的权重。根据属性的权重分布,我们使用一个基于实例的方法,而不同的行为是可能的。当权重为a.s.大于常数时,图中的边的权重和数量与loglog(n)成比例地增长,与图距离一样。另一方面,当描述通过图的第一通道渗透探索的连续时间分支过程在有限时间内到达无穷多个顶点时,权重收敛于两个i.i.d.随机变量表示从两个源开始的过程的爆炸时间。这种非普适性与度序列具有有限方差的设置形成鲜明对比(参见Bhamidi,Hofstad and Hooghiemstra arXiv:1210.6839)。

英文摘要:

We prove non-universality results for first-passage percolation on the configuration model with i.i.d. degrees having infinite variance. We focus on the weight of the optimal path between two uniform vertices. Depending on the properties of the weight distribution, we use an example-based approach and show that rather different behaviors are possible. When the weights are a.s. larger than a constant, the weight and number of edges in the graph grow proportionally to loglog(n), as for the graph distances. On the other hand, when the continuous-time branching process describing the first passage percolation exploration through the graph reaches infinitely many vertices in finite time, the weight converges to the sum of two i.i.d. random variables representing the explosion times of the processes started from the two sources. This non-universality is in sharp contrast to the setting where the degree sequence has a finite variance (see Bhamidi, Hofstad and Hooghiemstra arXiv: 1210.6839).

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