# Comparative analysis on the selection of number of clusters in community detection

arXiv: https://arxiv.org/abs/1606.07668 · HTML: https://mlsanity.com/paper/1606.07668/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2016.

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

- [Robust Markov stability for community detection at a scale learned based on the structure](https://mlsanity.com/paper/2504.11621/) (2025)

## Abstract (arXiv)

We conduct a comparative analysis on various estimates of the number of clusters in community detection. An exhaustive comparison requires testing of all possible combinations of frameworks, algorithms, and assessment criteria. In this paper we focus on the framework based on a stochastic block model, and investigate the performance of greedy algorithms, statistical inference, and spectral methods. For the assessment criteria, we consider modularity, map equation, Bethe free energy, prediction errors, and isolated eigenvalues. From the analysis, the tendency of overfit and underfit that the assessment criteria and algorithms have, becomes apparent. In addition, we propose that the alluvial diagram is a suitable tool to visualize statistical inference results and can be useful to determine the number of clusters.