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

TL;DR

This paper introduces GVE-Leiden, an optimized parallel implementation of the Leiden community detection algorithm that significantly outperforms existing versions in shared memory environments, enabling rapid analysis of large-scale networks.

Contribution

The paper presents GVE-Leiden, a highly efficient shared memory implementation of the Leiden algorithm, achieving unprecedented speedups over existing methods on multi-core servers.

Findings

GVE-Leiden outperforms other Leiden implementations by up to 436x.

Achieves a processing rate of 403 million edges per second on a 3.8 billion edge graph.

Performance improves by 1.6x with each doubling of threads.

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.