GVE-Leiden: Fast Leiden Algorithm for Community Detection in Shared Memory Setting
Abstract
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.
Cite
@article{arxiv.2312.13936,
title = {GVE-Leiden: Fast Leiden Algorithm for Community Detection in Shared Memory Setting},
author = {Subhajit Sahu},
journal= {arXiv preprint arXiv:2312.13936},
year = {2025}
}
Comments
13 pages, 10 figures, 2 tables. arXiv admin note: substantial text overlap with arXiv:2312.04876