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 multicore implementations of the Louvain algorithm, a high quality community detection method. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our Louvain, which we term as GVE-Louvain, outperforms Vite, Grappolo, and NetworKit Louvain by 50x, 22x, and 20x respectively - achieving a processing rate of 560M edges/s on a 3.8B edge graph. In addition, GVE-Louvain improves performance at an average rate of 1.6x for every doubling of threads.

翻译：社区检测是识别网络中自然划分的问题。在数据集规模已达到显著量级的众多应用中，高效识别此类划分的并行算法至关重要。本技术报告提出了Louvain算法（一种高质量社区检测方法）的最高效多核实现之一。在配备双16核Intel Xeon Gold 6226R处理器的服务器上，我们的Louvain（称为GVE-Louvain）相比Vite、Grappolo和NetworKit Louvain分别实现了50倍、22倍和20倍的性能提升——在包含38亿条边的图上达到了5.6亿条边/秒的处理速率。此外，线程数每增加一倍，GVE-Louvain的性能平均提升1.6倍。