Subnets of scale-free networks are not scale-free: Sampling properties of networks
Subnets of scale-free networks are not scale-free: Sampling properties of networks
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DOI：
10.1073/pnas.0501179102
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发表时间：
2005-03-22
影响因子：
11.1
通讯作者：
May, RM
中科院分区：
文献类型：
--
作者：
Stumpf, MPH;Wiuf, C;May, RM
大多数网络研究只关注真实网络的一小部分。在这里，我们讨论了在最简约抽样方案下网络度分布的抽样性质。只有当网络的度分布和随机抽样的概率分布属于同一个概率分布族时，才有可能从子网数据外推到全局网络的属性。我们表明，这个条件确实是满足一些重要的网络类，特别是经典的随机图和指数随机图。然而，对于无标度度分布，情况并非如此。因此，关于网络无标度性质的推论可能必须谨慎对待。这里提出的工作具有重要意义的分子网络的分析，以及图论和网络理论一般。
Most studies of networks have only looked at small subsets of the true network. Here, we discuss the sampling properties of a network's degree distribution under the most parsimonious sampling scheme. Only if the degree distributions of the network and randomly sampled subnets belong to the same family of probability distributions is it possible to extrapolate from subnet data to properties of the global network. We show that this condition is indeed satisfied for some important classes of networks, notably classical random graphs and exponential random graphs. For scale-free degree distributions, however, this is not the case. Thus, inferences about the scale-free nature of a network may have to be treated with some caution. The work presented here has important implications for the analysis of molecular networks as well as for graph theory and the theory of networks in general.