论文标题
一种适合复杂网络分布的可靠方法
A robust method for fitting degree distributions of complex networks
论文作者
论文摘要
这项工作介绍了一种拟合复杂网络数据集的度分布的方法，以便选择一组候选分布的最合适分布，同时最大程度地提高拟合模型的分布部分。当前适合文献分布的方法是不一致的，并且经常假定从中绘制数据的分布。非常重点放在分布的尾巴上，而尾部下方的大部分分布都被忽略了。重要的是要考虑这些低度节点，因为它们在渗透等过程中起着至关重要的作用。在这里，我们使用最大似然估计器来解决这些问题，以适合整个数据集或靠近它。该方法适用于任何网络数据集（或离散的经验数据集），我们在超过25个网络数据集中对其进行了测试，但除了几种情况外，还可以实现良好的合适。我们还证明了可能性的数值最大化比常用的分析近似值更好。此外，我们提供了一个可用于应用此方法的Python软件包。
This work introduces a method for fitting to the degree distributions of complex network datasets, such that the most appropriate distribution from a set of candidate distributions is chosen while maximizing the portion of the distribution to which the model is fit. Current methods for fitting to degree distributions in the literature are inconsistent and often assume a priori what distribution the data are drawn from. Much focus is given to fitting to the tail of the distribution, while a large portion of the distribution below the tail is ignored. It is important to account for these low degree nodes, as they play crucial roles in processes such as percolation. Here we address these issues, using maximum likelihood estimators to fit to the entire dataset, or close to it. This methodology is applicable to any network dataset (or discrete empirical dataset), and we test it on over 25 network datasets from a wide range of sources, achieving good fits in all but a few cases. We also demonstrate that numerical maximization of the likelihood performs better than commonly used analytical approximations. In addition, we have made available a Python package which can be used to apply this methodology.
