# GVE-Leiden: Fast Leiden Algorithm for Community Detection in Shared Memory Setting

arXiv: https://arxiv.org/abs/2312.13936 · HTML: https://mlsanity.com/paper/2312.13936/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2023.

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

- [Scalable Parameter-Light Spectral Method for Clustering Short Text Embeddings with a Cohesion-Based Evaluation Metric](https://mlsanity.com/paper/2511.19350/) (2025)
- [Structure-Induced Information for Rerooting Levin Tree Search](https://mlsanity.com/paper/2605.30664/) (2026)
- [ComNetX: Local Hierarchical Adaptation for Dynamic Community Detection](https://mlsanity.com/paper/2608.16906/) (2026)
- [CommunityKV: Efficient Long-Context Decoding via Graph Partitioning](https://mlsanity.com/paper/2610.00418/) (2026)

## Abstract (arXiv)

Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions is critical in a number of applications, where the size of datasets have reached significant scales. This technical report presents one of the most efficient implementations of the Leiden algorithm, a high quality community detection method. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our Leiden implementation, which we term as GVE-Leiden, outperforms the original Leiden, igraph Leiden, NetworKit Leiden, and cuGraph Leiden (running on NVIDIA A100 GPU) by 436x, 104x, 8.2x, and 3.0x respectively - achieving a processing rate of 403M edges/s on a 3.8B edge graph. In addition, GVE-Leiden improves performance at an average rate of 1.6x for every doubling of threads.