# Problems with classification, hypothesis testing, and estimator convergence in the analysis of degree distributions in networks

arXiv: https://arxiv.org/abs/2003.14012 · HTML: https://mlsanity.com/paper/2003.14012/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [Kosmos: An AI Scientist for Autonomous Discovery](https://mlsanity.com/paper/2511.02824/) (2025)

## Abstract (arXiv)

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.