Improved large-scale graph learning through ridge spectral sparsification
Abstract
Graph-based techniques and spectral graph theory have enriched the field of machine learning with a variety of critical advances. A central object in the analysis is the graph Laplacian L, which encodes the structure of the graph. We consider the problem of learning over this Laplacian in a distributed streaming setting, where new edges of the graph are observed in real time by a network of workers. In this setting, it is hard to learn quickly or approximately while keeping a distributed representation of L. To address this challenge, we present a novel algorithm, GSQUEAK, which efficiently sparsifies the Laplacian by maintaining a small subset of effective resistances. We show that our algorithm produces sparsifiers with strong spectral approximation guarantees, all while processing edges in a single pass and in a distributed fashion.
Cite
@article{arxiv.2604.20078,
title = {Improved large-scale graph learning through ridge spectral sparsification},
author = {Daniele Calandriello and Ioannis Koutis and Alessandro Lazaric and Michal Valko},
journal= {arXiv preprint arXiv:2604.20078},
year = {2026}
}
Comments
International Conference on Machine Learning (ICML 2018)