# Graph-based data clustering via multiscale community detection

arXiv: https://arxiv.org/abs/1909.04491 · HTML: https://mlsanity.com/paper/1909.04491/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

## Built on by (most cited first, 8 of 8)

- [A Multiscale Environment for Learning by Diffusion](https://mlsanity.com/paper/2102.00500/) (2021)
- [Multiscale methods for signal selection in single-cell data](https://mlsanity.com/paper/2206.07760/) (2022)
- [A Pipeline for Business Intelligence and Data-Driven Root Cause Analysis on Categorical Data](https://mlsanity.com/paper/2211.06717/) (2022)
- [Free Lunch for Privacy Preserving Distributed Graph Learning](https://mlsanity.com/paper/2305.10869/) (2023)
- [LGDE: Local Graph-based Dictionary Expansion](https://mlsanity.com/paper/2405.07764/) (2024)
- [Attributed Graph Clustering with Multi-Scale Weight-Based Pairwise Coarsening and Contrastive Learning](https://mlsanity.com/paper/2507.20505/) (2025)
- [Machine learning approaches for interpretable antibody property prediction using structural data](https://mlsanity.com/paper/2510.23975/) (2025)
- [Incremental Graph Construction Enables Robust Spectral Clustering of Texts](https://mlsanity.com/paper/2603.03056/) (2026)

## Abstract (arXiv)

We present a graph-theoretical approach to data clustering, which combines the creation of a graph from the data with Markov Stability, a multiscale community detection framework. We show how the multiscale capabilities of the method allow the estimation of the number of clusters, as well as alleviating the sensitivity to the parameters in graph construction. We use both synthetic and benchmark real datasets to compare and evaluate several graph construction methods and clustering algorithms, and show that multiscale graph-based clustering achieves improved performance compared to popular clustering methods without the need to set externally the number of clusters.