# Using synthetic networks for parameter tuning in community detection

## TL;DR

This paper introduces a method to tune community detection algorithms by generating synthetic networks that mimic real data, enabling parameter optimization without labeled data and improving detection quality.

## Contribution

The paper presents a novel approach to hyperparameter tuning for community detection using synthetic networks with known communities, applicable without labeled data.

## Key findings

- Significant improvements in community detection accuracy on synthetic datasets.
- Effective parameter tuning method applicable to various algorithms.
- Enhanced detection quality on real-world networks.

## Abstract

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.