Skip to main content

On the Complexity of Finding an Unknown Cut Via Vertex Queries

  • Conference paper
  • 998 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4598))

Abstract

We investigate the problem of finding an unknown cut through querying vertices of a graph G. Our complexity measure is the number of submitted queries. To avoid some worst cases, we make a few assumptions which allow us to obtain an algorithm with the worst case query complexity of \(O(k)+2k\log{n\over k}\) in which k is the number of vertices adjacent to cut-edges. We also provide a matching lowerbound and then prove if G is a tree our algorithm can asymptotically achieve the information theoretic lowerbound on the query complexity. Finally, we show it is possible to remove our extra assumptions but achieve an approximate solution.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Blum, A., Chawla, S.: Learning from labeled and unlabeled data using graph mincuts. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 19–26. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  2. Blum, A., Lafferty, J., Rwebangira, M.R., Reddy, R.: Semi-supervised learning using randomized mincuts. In: Proceedings of the twenty-first international conference on Machine learning, p. 13. ACM Press, New York (2004)

    Chapter  Google Scholar 

  3. Joachims, T.: Transductive learning via spectral graph partitioning. In: Twentieth International Conference on Machine Learning (2003)

    Google Scholar 

  4. Joachims, T.: Transductive learning via spectral graph partitioning. In: Proceedings of the International Conference on Machine Learning, pp. 290–297 (2003)

    Google Scholar 

  5. Kamvar, S., Klein, D., Manning, C.: Spectral learning. In: International Joint Conference On Artificial Intelligence (2003)

    Google Scholar 

  6. Kleinberg, J.: Detecting a network failure. In: Proceedings of the Forty-First Annual Symposium on Foundations of Computer Science, p. 231. IEEE Computer Society Press, Los Alamitos (2000)

    Google Scholar 

  7. Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  8. Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems (2001)

    Google Scholar 

  9. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)

    Article  Google Scholar 

  10. Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the Twentieth International Conference on Machine Learning, pp. 912–919 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Guohui Lin

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Afshani, P. et al. (2007). On the Complexity of Finding an Unknown Cut Via Vertex Queries. In: Lin, G. (eds) Computing and Combinatorics. COCOON 2007. Lecture Notes in Computer Science, vol 4598. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73545-8_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73545-8_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73544-1

  • Online ISBN: 978-3-540-73545-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics