# Review on Determining the Number of Communities in Network Data

arXiv: https://arxiv.org/abs/2503.00352 · HTML: https://mlsanity.com/paper/2503.00352/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [Coauthorship and Citation Networks for Statisticians](https://mlsanity.com/paper/1410.2840/) (2014)
- [Network Cross-Validation for Determining the Number of Communities in Network Data](https://mlsanity.com/paper/1411.1715/) (2014)
- [Hypothesis Testing for Automated Community Detection in Networks](https://mlsanity.com/paper/1311.2694/) (2013)
- [Enhancing Robustness of Gradient-Boosted Decision Trees through One-Hot Encoding and Regularization](https://mlsanity.com/paper/2304.13761/) (2023)
- [Hypothesis Testing for High-Dimensional Matrix-Valued Data](https://mlsanity.com/paper/2412.07987/) (2024)
- [A Short Note of Comparison between Convex and Non-convex Penalized Likelihood](https://mlsanity.com/paper/2502.07655/) (2025)

## Abstract (arXiv)

This paper reviews statistical methods for hypothesis testing and clustering in network models. We analyze the method by Bickel et al. (2016) for deriving the asymptotic null distribution of the largest eigenvalue, noting its slow convergence and the need for bootstrap corrections. The SCORE method by Jin et al. (2015) and the NCV method by Chen et al. (2018) are evaluated for their efficacy in clustering within Degree-Corrected Block Models, with NCV facing challenges due to its time-intensive nature. We suggest exploring eigenvector entry distributions as a potential efficiency improvement.