Skip to main content
SpringerLink
Log in

Graph Mining on Streams

  • Reference work entry
  • First Online:
Book cover Encyclopedia of Database Systems

  • 40 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 6,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  1. Aggarwal G, Datar M, Rajagopalan S, Ruhl M. On the streaming model augmented with a sorting primitive. In: Proceedings of the IEEE Symposium on Foundations of Computer Science; 2004. p. 540–9.

    Google Scholar 

  2. Bar-Yossef Z, Kumar R, Sivakumar D. Reductions in streaming algorithms, with an application to counting triangles in graphs. In: Proceedings of the 13th Annual ACM-SIAM Symposium on Discrete Algorithms; 2002. p. 623–32.

    Google Scholar 

  3. Buchsbaum AL, Giancarlo R, Westbrook J. On finding common neighborhoods in massive graphs. Theor Comput Sci. 2003;1–3(299):707–18.

    Article  MathSciNet  MATH  Google Scholar 

  4. Buriol LS, Frahling G, Leonardi S, Marchetti-Spaccamela A, Sohler C. Counting triangles in data streams. In: Proceedings of the 25th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2006. p. 253–62.

    Google Scholar 

  5. Chakrabarti A, Cormode G, McGregor A. A near-optimal algorithm for computing the entropy of a stream. In: Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms; 2007. p. 328–35.

    Google Scholar 

  6. Cormode G, Muthukrishnan S. Space efficient mining of multigraph streams. In: Proceedings of the 24th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2005. p. 271–82.

    Google Scholar 

  7. Das Sarma A, Gollapudi S, Panigrahy R. Estimating PageRank on graph streams. In: Proceedings of the 27th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2008. p. 69–78.

    Google Scholar 

  8. Demetrescu C, Escoffier B, Moruz G, Ribichini A. Adapting parallel algorithms to the w-stream model, with applications to graph problems. In: Proceedings of the International Symposium on Mathematical Foundations of Computer Science; 2007.p. 194–205.

    Google Scholar 

  9. Demetrescu C, Finocchi I, Ribichini A. Trading off space for passes in graph streaming problems. In: Proceedings of the 17th Annual ACM-SIAM Symposium on Discrete Algorithms; 2006. p. 714–23.

    Google Scholar 

  10. Elkin M. Streaming and fully dynamic centralized algorithms for constructing and maintaining sparse spanners. In: Proceedings of the 34th International Colloquium on Automata, Languages, and Programming; 2007. p. 716–27.

    Google Scholar 

  11. Elkin M, Zhang J. Efficient algorithms for constructing (1 + ε, β)-spanners in the distributed and streaming models. Distrib Comput. 2006;18(5):375–85.

    Article  MATH  Google Scholar 

  12. Feigenbaum J, Kannan S, McGregor A, Suri S, Zhang J. Graph distances in the data-stream model. SIAM J Comput. 2008;38(5):1708–27.

    MathSciNet  MATH  Google Scholar 

  13. Feigenbaum J, Kannan S, McGregor A, Suri S, Zhang J. On graph problems in a semi-streaming model. Theor Comput Sci. 2005;348(2–3):207–16.

    Article  MathSciNet  MATH  Google Scholar 

  14. Ganguly S, Saha B. On estimating path aggregates over streaming graphs. In: Proceedings of the International Symposium on Algorithms and Computation; 2006. p. 163–72.

    Chapter  Google Scholar 

  15. Henzinger MR, Raghavan P, Rajagopalan S. Computing on data streams. In: External memory algorithms. Boston: American Mathematical Society; 1999. p. 107–18.

    Chapter  Google Scholar 

  16. McGregor A. Finding graph matchings in data streams. In: APPROX-RANDOM; 2005. p. 170–81.

    Google Scholar 

  17. Muthukrishnan S. Data streams: algorithms and applications. Foundations and trends in theoretical computer science, vol. 1-2. Hanover: Now; 2005.

    Article  MathSciNet  MATH  Google Scholar 

  18. Zelke M. k-connectivity in the semi-streaming model. CoRR, cs/0608066. 2006.

    Google Scholar 

  19. Zelke M. Weighted matching in the semi-streaming model. In: Proceedings of the Symposium on Theoretical Aspects of Computer Science; 2008.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Microsoft Research, Silicon Valley, Mountain View, CA, USA

    Andrew McGregor

Authors
  1. Andrew McGregor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew McGregor .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. AT&T Labs - Research, AT&T, Bedminster, NJ, USA

    Divesh Srivastava

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

McGregor, A. (2018). Graph Mining on Streams. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_184

Download citation

Publish with us

Policies and ethics

Search

Navigation