Real-world graphs often evolve over time, making community or cluster detection a crucial task. In this technical report, we extend three dynamic approaches - Naive-dynamic (ND), Delta-screening (DS), and Dynamic Frontier (DF) - to our multicore implementation of the Leiden algorithm, known for its high-quality community detection. Our experiments, conducted on a server with a 64-core AMD EPYC-7742 processor, show that ND, DS, and DF Leiden achieve average speedups of 1.37x, 1.47x, and 1.98x on large graphs with random batch updates, and 1.07x, 1.10x, and 1.13x on real-world dynamic graphs, compared to the Static Leiden algorithm. To our knowledge, this is the first attempt to apply dynamic approaches to the Leiden algorithm. We hope these early results pave the way for further development of dynamic approaches for evolving graphs.

翻译：现实世界中的图结构往往随时间演变，这使得社区或簇检测成为一项关键任务。在本技术报告中，我们将三种动态方法——朴素动态（ND）、增量筛选（DS）和动态前沿（DF）——扩展到我们多核实现的Leiden算法中，该算法以其高质量的社区检测能力而闻名。我们在配备64核AMD EPYC-7742处理器的服务器上进行的实验表明，与静态Leiden算法相比，ND、DS和DF Leiden算法在具有随机批量更新的大规模图上分别实现了1.37倍、1.47倍和1.98倍的平均加速比，在真实世界动态图上则分别达到1.07倍、1.10倍和1.13倍。据我们所知，这是首次将动态方法应用于Leiden算法的尝试。我们希望这些初步成果能为演化图动态方法的进一步发展铺平道路。