HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering

TL;DR

HyperEF is a scalable spectral hypergraph coarsening framework that efficiently decomposes large hypergraphs into clusters using effective resistance estimation, preserving spectral properties and significantly improving runtime.

Contribution

Introduces HyperEF, a nearly-linear time algorithm for hyperedge effective resistance estimation enabling scalable spectral hypergraph coarsening with novel diffusion-based operators.

Findings

Achieves over 70x speedup compared to hMetis

More effectively preserves spectral properties

Enables larger hypergraph decompositions

Abstract

This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge effective resistances. Motivated by the latest theoretical framework for low-resistance-diameter decomposition of simple graphs, HyperEF aims at decomposing large hypergraphs into multiple node clusters with only a few inter-cluster hyperedges. The key component in HyperEF is a nearly-linear time algorithm for estimating hyperedge effective resistances, which allows incorporating the latest diffusion-based non-linear quadratic operators defined on hypergraphs. To achieve good runtime scalability, HyperEF searches within the Krylov subspace (or approximate eigensubspace) for identifying the nearly-optimal vectors for approximating the hyperedge effective resistances. In addition, a node weight propagation scheme for multilevel spectral hypergraph decomposition has been introduced for achieving even greater node coarsening ratios. When compared with state-of-the-art hypergraph partitioning (clustering) methods, extensive experiment results on real-world VLSI designs show that HyperEF can more effectively coarsen (decompose) hypergraphs without losing key structural (spectral) properties of the original hypergraphs, while achieving over $70\times$ runtime speedups over hMetis and $20\times$ speedups over HyperSF.