  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.
