Simple parallel and distributed algorithms for spectral graph sparsification

Ioannis Koutis, Shen Chen Xu

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


We describe simple algorithms for spectral graph sparsification, based on iterative computations of weighted spanners and sampling. Leveraging the algorithms of Baswana and Sen for computing spanners, we obtain the first distributed spectral sparsification algorithm in the CONGEST model.We also obtain a parallel algorithm with improved work and time guarantees, as well as other natural distributed implementations. Combining this algorithm with the parallel framework of Peng and Spielman for solving symmetric diagonally dominant linear systems, we get a parallel solver that is significantlymore efficient in terms of the total work.

Original languageEnglish (US)
Article numbera14
JournalACM Transactions on Parallel Computing
Issue number2
StatePublished - Aug 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Hardware and Architecture
  • Computer Science Applications
  • Computational Theory and Mathematics


  • SDD linear systems
  • Sparsest cut
  • Spectral sparsification


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