AI 中文总结
该研究提出pLouvain与pLeiden两种GPU并行实现，其中pLeiden可保留串行Leiden的质量保证，二者在57个图上分别实现3.1倍、8.8倍加速，pLouvain模块度表现最优，其子程序可辅助其他Louvain技术并行化。
AI 中文摘要
串行Louvain和Leiden算法是用于大型图中模块度优化聚类（或社区检测）的广泛技术。我们提出了pLouvain和pLeiden这两种新的GPU并行实现。pLouvain基于Louvain+扩展；pLeiden是首个可证明保留串行Leiden所有质量保证的并行实现，我们通过一种新颖的基于生成树的细化方法实现了这一点。pLouvain和pLeiden均使用轻量级的对称性破坏技术来模拟顶点的有序遍历。对于pLouvain，我们开发了一种替代迭代策略，以纠正Louvain/Louvain+中观察到的内部集群连通性较弱的问题。此外，pLouvain和pLeiden均优化了LambdaCC目标函数，该函数是模块度及相关Constant Potts模型的泛化。在来自10个家族的57个图组成的集合上，我们的结果显示，pLouvain和pLeiden分别比当前最快的Louvain和Leiden开源并行实现达到了3.1倍和8.8倍的几何平均加速比。对于生成的聚类，pLouvain在几乎所有测试图上都产生了最高的模块度分数。这两种多级方法中的子例程可助力其他基于Louvain的技术的并行化。
英文摘要
The sequential Louvain and Leiden algorithms are widely used techniques for modularity-optimizing clustering (or community detection) in large graphs. We present pLouvain and pLeiden, two new GPU parallelizations. pLouvain is based on the Louvain+ extension. pLeiden is the first parallel implementation to provably preserve all quality guarantees of sequential Leiden. We achieve this through a novel spanning-tree-based refinement approach. Both pLouvain and pLeiden use a lightweight symmetry-breaking technique that emulates an ordered traversal of vertices. For pLouvain, we develop an alternative iteration strategy to rectify the weak internal cluster connectivity observed in Louvain/Louvain+. Further, both pLouvain and pLeiden optimize the LambdaCC objective function, a generalization of modularity and the related Constant Potts model. On a collection of 57 graphs from 10 families, our results show that pLouvain and pLeiden achieve geometric mean speedups of 3.1x and 8.8x, respectively, over the current fastest open-source parallelizations of Louvain and Leiden. For the clusterings generated, pLouvain yields the highest modularity scores on nearly all tested graphs. The subroutines within these two multilevel approaches could aid in the parallelization of other Louvain-based techniques.
CommentsPublished at 2026 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Journal refM. S. Gilbert and K. Madduri, "GPU-Accelerated Multilevel Graph Clustering: A Parallel Perspective on Louvain and Leiden," 2026 IEEE International Parallel and Distributed Processing Symposium (IPDPS), New Orleans, LA, USA, 2026, pp. 87-99
DOI：10.1109/IPDPS65963.2026.00020
