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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.
Sahu et al. (Thu,) studied this question.