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arXiv · 2412.13592

PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms

Etienne Lasalle·Rémi Vaudaine·Titouan Vayer·Pierre Borgnat·Rémi Gribonval·Paulo Gonçalves·Màrton Karsai
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Abstract

Clustering the nodes of a graph is a cornerstone of graph analysis and has been extensively studied. However, some popular methods are not suitable for very large graphs: e.g., spectral clustering requires the computation of the spectral decomposition of the Laplacian matrix, which is not applicable for large graphs with a large number of communities. This work introduces PASCO, an overlay that accelerates clustering algorithms. Our method consists of three steps: 1-We compute several independent small graphs representing the input graph by applying an efficient and structure-preserving coarsening algorithm. 2-A clustering algorithm is run in parallel onto each small graph and provides several partitions of the initial graph. 3-These partitions are aligned and combined with an optimal transport method to output the final partition. The PASCO framework is based on two key contributions: a novel global algorithm structure designed to enable parallelization and a fast, empirically validated graph coarsening algorithm that preserves structural properties. We demonstrate the strong performance of 1 PASCO in terms of computational efficiency, structural preservation, and output partition quality, evaluated on both synthetic and real-world graph datasets.

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Etienne Lasalle, Rémi Vaudaine, Titouan Vayer, Pierre Borgnat, Rémi Gribonval, Paulo Gonçalves, Màrton Karsai. 2025-06-12. PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms. https://arxiv.org/abs/2412.13592

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