Community detection is the problem of identifying densely connected clusters of nodes within a network. The Louvain algorithm is a widely used method for this task, but it can produce communities that are internally disconnected. To address this, the Leiden algorithm was introduced. In this technical report, we propose another approach to mitigate this issue. On a system with two 16-core Intel Xeon Gold 6226R processors, our new parallel algorithm GSP-Louvain, based on the Louvain algorithm, addresses this issue, and outperforms the original Leiden, igraph Leiden, and NetworKit Leiden by 341x, 83x, and 6.1x respectively - achieving a processing rate of 328M edges/s on a 3.8B edge graph. Furthermore, GSP-Louvain improves performance at a rate of 1.5x for every doubling of threads.

翻译：社群检测是识别网络中密集连接节点簇的问题。Louvain算法是广泛用于该任务的方法，但其可能产生内部分裂的社群。为解决此问题，Leiden算法被提出。在本技术报告中，我们提出另一种缓解该问题的方法。在配备两个16核Intel Xeon Gold 6226R处理器的系统上，我们基于Louvain算法的新并行算法GSP-Louvain成功解决了内部分裂问题，其性能较原始Leiden、igraph Leiden及NetworKit Leiden分别提升341倍、83倍和6.1倍——在38亿条边的图上实现了每秒3.28亿条边的处理速率。此外，GSP-Louvain在线程数翻倍时性能提升1.5倍。