Community detection is the problem of identifying tightly connected clusters of nodes within a network. Efficient parallel algorithms for this play a crucial role in various applications, especially as datasets expand to significant sizes. The Label Propagation Algorithm (LPA) is commonly employed for this purpose due to its ease of parallelization, rapid execution, and scalability. However, it may yield internally disconnected communities. This technical report introduces GSL-LPA, derived from our parallelization of LPA, namely GVE-LPA. Our experiments on a system with two 16-core Intel Xeon Gold 6226R processors show that GSL-LPA not only mitigates this issue but also surpasses FLPA, igraph LPA, and NetworKit LPA by 55x, 10,300x, and 5.8x, respectively, achieving a processing rate of 844 M edges/s on a 3.8 B edge graph. Additionally, GSL-LPA scales at a rate of 1.6x for every doubling of threads.

翻译：社区检测是从网络中识别紧密连接节点簇的问题。针对该问题的高效并行算法在各类应用中至关重要，尤其是当数据集规模显著扩大时。标签传播算法因其易于并行化、执行速度快且可扩展性强而被广泛采用。然而，该算法可能产生内部不连通的社区。本技术报告提出GSL-LPA算法，该算法源于我们对LPA的并行化实现GVE-LPA。我们在配备双路16核英特尔至强金牌6226R处理器的系统上进行的实验表明，GSL-LPA不仅解决了上述问题，其性能还分别超过FLPA、igraph LPA和NetworKit LPA达55倍、10,300倍和5.8倍，在38亿条边的图上实现了8.44亿条边/秒的处理速度。此外，GSL-LPA的加速比随线程数翻倍以1.6倍比例提升。