Large-scale semi-supervised learning with online spectral graph sparsification

arXiv cs.LG / 4/30/2026

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Key Points

  • The paper proposes Sparse-HFS, a scalable semi-supervised learning (SSL) algorithm based on online spectral graph sparsification.
  • It claims the method can solve SSL problems using only O(n polylog(n)) memory while running in O(m polylog(n)) time.
  • The approach is designed to make SSL computationally practical at large scale by reducing the cost of working with large graphs.
  • The work is submitted as an arXiv preprint (arXiv:2604.26550) under Machine Learning (cs.LG), indicating early-stage dissemination for further research and validation.

Computer Science > Machine Learning

arXiv:2604.26550 (cs)
[Submitted on 29 Apr 2026]

Title:Large-scale semi-supervised learning with online spectral graph sparsification

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Abstract:We introduce Sparse-HFS, a scalable algorithm that can compute solutions to SSL problems using only O(n polylog(n)) space and O(m polylog(n)) time.
Comments:
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2604.26550 [cs.LG]
  (or arXiv:2604.26550v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2604.26550
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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Michal Valko [view email]
[v1] Wed, 29 Apr 2026 11:32:57 UTC (333 KB)
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