# Fast unfolding of communities in large networks: 15 years later

arXiv: https://arxiv.org/abs/2311.06047 · HTML: https://mlsanity.com/paper/2311.06047/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2023.

## Built on by (most cited first, 6 of 6)

- [Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning Policies](https://mlsanity.com/paper/2501.03142/) (2025)
- [From Leiden to Pleasure Island: The Constant Potts Model for Community Detection as a Hedonic Game](https://mlsanity.com/paper/2509.03834/) (2025)
- [Disentangled and Self-Explainable Node Representation Learning](https://mlsanity.com/paper/2410.21043/) (2024)
- [Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation](https://mlsanity.com/paper/2504.13614/) (2025)
- [A Graph Machine Learning Approach for Detecting Topological Patterns in Transactional Graphs](https://mlsanity.com/paper/2509.12730/) (2025)
- [End-to-End Shared Attention Estimation via Group Detection with Feedback Refinement](https://mlsanity.com/paper/2604.01714/) (2026)

## Abstract (arXiv)

The Louvain method was proposed 15 years ago as a heuristic method for the fast detection of communities in large networks. During this period, it has emerged as one of the most popular methods for community detection, the task of partitioning vertices of a network into dense groups, usually called communities or clusters. Here, after a short introduction to the method, we give an overview of the different generalizations and modifications that have been proposed in the literature, and also survey the quality functions, beyond modularity, for which it has been implemented.