pLouvain and pLeiden, two GPU parallelizations, speed up Louvain and Leiden clustering by 3.1x and 8.8x, and pLeiden's spanning-tree refinement claims to preserve all of sequential Leiden's quality guarantees.
Memetic Graph Clustering
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abstract
It is common knowledge that there is no single best strategy for graph clustering, which justifies a plethora of existing approaches. In this paper, we present a general memetic algorithm, VieClus, to tackle the graph clustering problem. This algorithm can be adapted to optimize different objective functions. A key component of our contribution are natural recombine operators that employ ensemble clusterings as well as multi-level techniques. Lastly, we combine these techniques with a scalable communication protocol, producing a system that is able to compute high-quality solutions in a short amount of time. We instantiate our scheme with local search for modularity and show that our algorithm successfully improves or reproduces all entries of the 10th DIMACS implementation~challenge under consideration using a small amount of time.
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2026 1verdicts
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GPU-Accelerated Multilevel Graph Clustering: A Parallel Perspective on Louvain and Leiden
pLouvain and pLeiden, two GPU parallelizations, speed up Louvain and Leiden clustering by 3.1x and 8.8x, and pLeiden's spanning-tree refinement claims to preserve all of sequential Leiden's quality guarantees.