# Different approaches to community detection

arXiv: https://arxiv.org/abs/1712.06468 · HTML: https://mlsanity.com/paper/1712.06468/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2017.

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

- [Modularity maximisation for graphons](https://mlsanity.com/paper/2101.00503/) (2021)
- [Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation](https://mlsanity.com/paper/2411.14696/) (2024)
- [Synwalk -- Community Detection via Random Walk Modelling](https://mlsanity.com/paper/2101.08623/) (2021)
- [The Hyperspherical Geometry of Community Detection: Modularity as a Distance](https://mlsanity.com/paper/2107.02645/) (2021)

## Abstract (arXiv)

A precise definition of what constitutes a community in networks has remained elusive. Consequently, network scientists have compared community detection algorithms on benchmark networks with a particular form of community structure and classified them based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different reasons for why we would want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different approaches to community detection also delineates the many lines of research and points out open directions and avenues for future research.