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arXiv 1908.07066math.PRcond-mat.stat-mechcs.DMcs.SI
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随机阈值图的渐近度分布

Asymptotic degree distributions in random threshold graphs

Armand M. Makowski, Siddharth Pal

摘要:

本文讨论了一类随机阈值图在多结点情况下的几种极限度分布。这种分析是在对潜在适应度变量分布的弱假设下进行的。由指数分布满足的该假设确定了自然标度,在该自然标度下示出了以下限制结果:节点度分布,即,任何节点的分布在分布上收敛于极限PMF。然而,对于每个$d=0,1, \ldots $,具有给定度$d$的节点的分数仅在分布上收敛到非退化随机变量$Π(d)$(其分布取决于$d$),而不是如通常预期的那样在概率上收敛到上述限制节点pmf。仅通过其特征函数识别$Π(d)$的分布。这一结果的含义包括:(i)经验节点分布不能作为极限节点pmf的替代或估计;(ii)即使在齐次图中,网络度分布和节点度分布也可能捕获截然不同的信息;和(iii)具有指数分布适应度的随机阈值图不提供Barabási的替代无标度模型,阿尔伯特模型,正如一些作者所认为的那样;这两个模型在度分布方面不能进行有意义的比较!

英文摘要:

We discuss several limiting degree distributions for a class of random threshold graphs in the many node regime. This analysis is carried out under a weak assumption on the distribution of the underlying fitness variable. This assumption, which is satisfied by the exponential distribution, determines a natural scaling under which the following limiting results are shown: The nodal degree distribution, i.e., the distribution of any node, converges in distribution to a limiting pmf. However, for each $d=0,1, \ldots $, the fraction of nodes with given degree $d$ converges only in distribution to a non-degenerate random variable $Π(d)$ (whose distribution depends on $d$),and not in probability to the aforementioned limiting nodal pmf as is customarily expected. The distribution of $Π(d)$ is identified only through its characteristic function. Implications of this result include: (i) The empirical node distribution may not be used as a proxy for or as an estimate to the limiting nodal pmf; (ii) Even in homogeneous graphs, the network-wide degree distribution and the nodal degree distribution may capture vastly different information; and (iii) Random threshold graphs with exponential distributed fitness do not provide an alternative scale-free model to the Barabási-Albert model as was argued by some authors; the two models cannot be meaningfully compared in terms of their degree distributions!

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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