# Using synthetic networks for parameter tuning in community detection

arXiv: https://arxiv.org/abs/1906.04555 · HTML: https://mlsanity.com/paper/1906.04555/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [A multilevel clustering technique for community detection](https://mlsanity.com/paper/2101.06551/) (2021)
- [The Hyperspherical Geometry of Community Detection: Modularity as a Distance](https://mlsanity.com/paper/2107.02645/) (2021)
- [Dissecting graph measure performance for node clustering in LFR parameter space](https://mlsanity.com/paper/2202.09827/) (2022)
- [Graph clustering with Boltzmann machines](https://mlsanity.com/paper/2203.02471/) (2022)

## Abstract (arXiv)

Community detection is one of the most important and challenging problems in network analysis. However, real-world networks may have very different structural properties and communities of various nature. As a result, it is hard (or even impossible) to develop one algorithm suitable for all datasets. A standard machine learning tool is to consider a parametric algorithm and choose its parameters based on the dataset at hand. However, this approach is not applicable to community detection since usually no labeled data is available for such parameter tuning. In this paper, we propose a simple and effective procedure allowing to tune hyperparameters of any given community detection algorithm without requiring any labeled data. The core idea is to generate a synthetic network with properties similar to a given real-world one, but with known communities. It turns out that tuning parameters on such synthetic graph also improves the quality for a given real-world network. To illustrate the effectiveness of the proposed algorithm, we show significant improvements obtained for several well-known parametric community detection algorithms on a variety of synthetic and real-world datasets.