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 paper 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 NetworKit Leiden and cuGraph Leiden (running on NVIDIA A100 GPU) by 8.2 × and 3.0 × respectively - achieving a processing rate of 403M edges/s on a 3.8B edge graph. In addition, GVE-Leiden improves performance at a rate of 1.6 × for every doubling of threads.