论文标题
分类，假设检验和估计量收敛的问题在网络中的程度分布分析中
Problems with classification, hypothesis testing, and estimator convergence in the analysis of degree distributions in networks
论文作者
论文摘要
在最近的工作“无标度网络很少见”的工作中，Broido和Clauset解决了网络中学位分布的分析问题，以将其归类为“规模柔性”的不同优势。在过去的二十年中，网络科学中的众多论文报告说，许多现实世界网络中的学位分布遵循权力法。然后将这样的网络称为无标度。但是，由于缺乏精确的定义，该术语已演变为意味着一系列不同的事物，从而导致有关给定网络规模繁琐的混乱和矛盾的主张。认识到这个问题，“无尺度网络是罕见的”作者尝试修复它。他们试图开发一种多功能和统计原则的方法，以消除网络科学文献中积累的这种无规模的歧义。尽管他们的论文提出了解决这个基本问题的公平尝试，但我们必须将注意力集中在其中的一些重要问题上。
In their recent work "Scale-free networks are rare", Broido and Clauset address the problem of the analysis of degree distributions in networks to classify them as scale-free at different strengths of "scale-freeness." Over the last two decades, a multitude of papers in network science have reported that the degree distributions in many real-world networks follow power laws. Such networks were then referred to as scale-free. However, due to a lack of a precise definition, the term has evolved to mean a range of different things, leading to confusion and contradictory claims regarding scale-freeness of a given network. Recognizing this problem, the authors of "Scale-free networks are rare" try to fix it. They attempt to develop a versatile and statistically principled approach to remove this scale-free ambiguity accumulated in network science literature. Although their paper presents a fair attempt to address this fundamental problem, we must bring attention to some important issues in it.
