# Hybrid Graph Embeddings and Louvain Algorithm for Unsupervised Community Detection

arXiv: https://arxiv.org/abs/2509.23411 · HTML: https://mlsanity.com/paper/2509.23411/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks](https://mlsanity.com/paper/2007.02901/) (2020)
- [GEMSEC: Graph Embedding with Self Clustering](https://mlsanity.com/paper/1802.03997/) (2018)
- [Overlapping Community Detection with Graph Neural Networks](https://mlsanity.com/paper/1909.12201/) (2019)
- [Supervised Community Detection with Line Graph Neural Networks](https://mlsanity.com/paper/1705.08415/) (2017)
- [A General Optimization Technique for High Quality Community Detection in Complex Networks](https://mlsanity.com/paper/1308.3508/) (2013)
- [Faster unfolding of communities: speeding up the Louvain algorithm](https://mlsanity.com/paper/1503.01322/) (2015)
- [A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature](https://mlsanity.com/paper/2401.02542/) (2024)
- [Contrastive Deep Nonnegative Matrix Factorization for Community Detection](https://mlsanity.com/paper/2311.02357/) (2023)
- [An improvement on the Louvain algorithm using random walks](https://mlsanity.com/paper/2403.08313/) (2024)

## Built on by (most cited first, 1 of 1)

- [Clustering-Based Collective Anomaly Detection in IoT Systems: A Graph Neural Network Approach](https://mlsanity.com/paper/2609.22166/) (2026)

## Abstract (arXiv)

This paper proposes a novel community detection method that integrates the Louvain algorithm with Graph Neural Networks (GNNs), enabling the discovery of communities without prior knowledge. Compared to most existing solutions, the proposed method does not require prior knowledge of the number of communities. It enhances the Louvain algorithm using node embeddings generated by a GNN to capture richer structural and feature information. Furthermore, it introduces a merging algorithm to refine the results of the enhanced Louvain algorithm, reducing the number of detected communities. To the best of our knowledge, this work is the first one that improves the Louvain algorithm using GNNs for community detection. The improvement of the proposed method was empirically confirmed through an evaluation on real-world datasets. The results demonstrate its ability to dynamically adjust the number of detected communities and increase the detection accuracy in comparison with the benchmark solutions.