Single pass spectral sparsification in dynamic streams

Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford

Research output: Chapter in Book/Report/Conference proceedingConference contribution

60 Scopus citations


We present the first single pass algorithm for computing spectral sparsifiers of graphs in the dynamic semi-streaming model. Given a single pass over a stream containing insertions and deletions of edges to a graph, G, our algorithm maintains a randomized linear sketch of the incidence matrix into dimension O((1/ε2) n polylog(n)). Using this sketch, the algorithm can output a (1 +/-ε) spectral sparsifier for G with high probability. While O((1/ε2) n polylog(n)) space algorithms are known for computing cut sparsifiers in dynamic streams [AGM12b, GKP12] and spectral sparsifiers in insertion-only streams [KL11], prior to our work, the best known single pass algorithm for maintaining spectral sparsifiers in dynamic streams required sketches of dimension ω((1/ε2) n(5/3)) [AGM14]. To achieve our result, we show that, using a coarse sparsifier of G and a linear sketch of G's incidence matrix, it is possible to sample edges by effective resistance, obtaining a spectral sparsifier of arbitrary precision. Sampling from the sketch requires a novel application of ℓ2/ℓ2 sparse recovery, a natural extension of the ℓ0 methods used for cut sparsifiers in [AGM12b]. Recent work of [MP12] on row sampling for matrix approximation gives a recursive approach for obtaining the required coarse sparsifiers. Under certain restrictions, our approach also extends to the problem of maintaining a spectral approximation for a general matrix AT A given a stream of updates to rows in A.

Original languageEnglish (US)
Title of host publicationProceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS
PublisherIEEE Computer Society
Number of pages10
ISBN (Electronic)9781479965175
StatePublished - Dec 7 2014
Event55th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2014 - Philadelphia, United States
Duration: Oct 18 2014Oct 21 2014

Publication series

NameProceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS
ISSN (Print)0272-5428


Other55th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2014
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • General Computer Science


  • dimensionality reduction
  • sketching
  • sparse recovery
  • sparsification
  • streaming


Dive into the research topics of 'Single pass spectral sparsification in dynamic streams'. Together they form a unique fingerprint.

Cite this