Sampling Based Estimation of In-Degree Distribution for Directed Complex Networks
Sampling Based Estimation of In-Degree Distribution for Directed Complex Networks
复制标题
有向复杂网络度分布的基于采样的估计
DOI：
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发表时间：
2021
影响因子：
2.4
通讯作者：
Bang Wang
中科院分区：
文献类型：
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作者：
Nelson Antunes;S. Bhamidi;Tianjian Guo;V. Pipiras;Bang Wang
摘要本文的重点是从抽样网络节点或边估计有向网络的入度分布。一些抽样方案被认为是，包括随机抽样和不更换，和几种方法的基础上随机游走与可能的跳跃。当对节点进行采样时，假设只有该节点的外边缘是可见的，也就是说，没有观察到该节点的入度。建议估计的入度分布是基于两种方法。反演方法利用原始和样本入度分布之间的关系，并且可以估计入度分布的大部分，但不能估计分布的尾部。入度分布的尾部通过渐近方法估计，该方法本身有两个版本：一个假设幂律尾部，另一个假设一般形式的尾部。这两种估计方法在合成和真实的网络上进行了检验，具有良好的性能结果，特别是渐近方法。本文的补充文件可在线获得。
ABSTRACT The focus of this work is on estimation of the in-degree distribution in directed networks from sampling network nodes or edges. A number of sampling schemes are considered, including random sampling with and without replacement, and several approaches based on random walks with possible jumps. When sampling nodes, it is assumed that only the out-edges of that node are visible, that is, the in-degree of that node is not observed. The suggested estimation of the in-degree distribution is based on two approaches. The inversion approach exploits the relation between the original and sample in-degree distributions, and can estimate the bulk of the in-degree distribution, but not the tail of the distribution. The tail of the in-degree distribution is estimated through an asymptotic approach, which itself has two versions: one assuming a power-law tail and the other for a tail of general form. The two estimation approaches are examined on synthetic and real networks, with good performance results, especially striking for the asymptotic approach. Supplementary files for this article are available online.