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arxiv: 1609.03769 · v1 · submitted 2016-09-13 · 📊 stat.ML · cs.DS· cs.LG

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Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting

Daniele Calandriello , Alessandro Lazaric , Michal Valko
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classification 📊 stat.ML cs.DScs.LG
keywords algorithmanalysiskelnerlevinaccountacrossdependenciesderive
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We derive a new proof to show that the incremental resparsification algorithm proposed by Kelner and Levin (2013) produces a spectral sparsifier in high probability. We rigorously take into account the dependencies across subsequent resparsifications using martingale inequalities, fixing a flaw in the original analysis.

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  1. Improved large-scale graph learning through ridge spectral sparsification

    cs.LG 2026-04 unverdicted novelty 5.0

    GSQUEAK produces spectrally accurate sparsifiers for graph Laplacians in a single-pass distributed streaming setting.