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 an optimized parallel implementation of Louvain, a high quality community detection method, for shared memory multicore systems. 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。高效并行算法对于识别这类划分在诸多数据集规模已达到显著尺度的应用中至关重要。本技术报告提出了一种针对共享内存多核系统的高质量社区检测方法——鲁汶算法的优化并行实现。在配备双路16核Intel Xeon Gold 6226R处理器的服务器上，我们提出的GVE-Louvain方法分别比Vite、Grappolo和NetworKit Louvain快50倍、22倍和20倍——在38亿条边的图上实现了5.6亿条边/秒的处理速率。此外，线程数每翻一倍，GVE-Louvain的性能平均提升1.6倍。