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

TL;DR

PASCO is a novel overlay framework that accelerates large-scale graph clustering by combining parallel coarsening, multiple clustering runs, and optimal transport for partition alignment, significantly improving efficiency and quality.

Contribution

This work introduces PASCO, a new parallel graph coarsening and clustering framework that preserves structure and enhances scalability for large graphs.

Findings

PASCO significantly reduces clustering computation time.

It maintains high structural fidelity in the resulting partitions.

It outperforms existing methods on synthetic and real-world datasets.

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.