Advertisement

Classifying Special Interest Groups in Web Graphs

  • Colin Cooper
Conference paper
  • 715 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2483)

Abstract

We consider the problem of classifying special interest groups in web graphs. There is a secret society of blue vertices which link preferentially to each other. The other vertices, which are red, are unaware of the distinction between vertex colours and link to vertices arbitrarily. Each new vertex directs m edges towards the existing graph on joining it. The colour of the vertices is unknown. We give an algorithm which whp classifies all blue vertices, and all red vertices of high degree correctly. We also give an upper bound for the number of mis-classified red vertices.

Keywords

Random Graph Special Interest Group Vertex Degree Degree Sequence Vertex Colour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    D. Achlioptas, A. Fiat, A.R. Karlin and F. McSherry, Web search via hub synthesis, Proceedings of the 42nd Annual IEEE Symposium on Foundations of Computer Science (2001) 500–509.Google Scholar
  2. 2.
    M. Adler and M. Mitzenmacher, Toward Compressing Web Graphs, To appear in the 2001 Data Compression Conference.Google Scholar
  3. 3.
    W. Aiello, F. Chung and L. Lu, Random evolution in massive graphs, Proceedings of the 42nd Annual IEEE Symposium on Foundations of Computer Science (2001) 510–519.Google Scholar
  4. 4.
    R. Albert, A. Barabasi and H. Jeong. Diameter of the world wide web. Nature 401:103–131 (1999) see also http://xxx.lanl.gov/abs/cond-mat/9907038 Google Scholar
  5. 5.
    B. Bollob’as, O. Riordan and J. Spencer, The degree sequence of a scale free random graph process, to appear.Google Scholar
  6. 6.
    B. Bollobás and O. Riordan, The diameter of a scale free random graph, to appear.Google Scholar
  7. 7.
    A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins and J. Wiener. Graph structure in the web. http://gatekeeper.dec.com/pub/DEC/SRC/publications/stata/www9.htm
  8. 8.
    A. Broder, R. Krauthgamer and M. Mitzenmacher. Improved classification via connectivity information.Google Scholar
  9. 9.
    S. Chakrabarti, B. Dom and P. Indyk. Enhanced hypertext categorization using hyperlinks. Proceedings of ACM SIGMOD Conference on management of Data (1998) p307–318.Google Scholar
  10. 10.
    C. Cooper and A.M. Frieze, A general model of web graphs, Proceedings of ESA 2001, 500–511.Google Scholar
  11. 11.
    E. Drinea, M. Enachescu and M. Mitzenmacher, Variations on random graph models for the web.Google Scholar
  12. 12.
    M.R. Henzinger, A. Heydon, M. Mitzenmacher and M. Najork, Measuring Index Quality Using Random Walks on the Web, WWW8 / Computer Networks 31 (1999) 1291–1303.CrossRefGoogle Scholar
  13. 13.
    W. Hoeffding, Probability inequalities for sums of bounded random variables, Journal of the American Statistical Association 58 (1963) 13–30.zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    R. Kumar, P. Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins and E. Upfal. The web as a graph. http://www.almaden.ibm.com
  15. 15.
    R. Kumar, P. Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins and E. Upfal. Stochastic models for the web graph. http://www.almaden.ibm.com
  16. 16.
    F. McSherry. Spectral partitioning of random graphs FOCS 2001Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Colin Cooper
    • 1
  1. 1.Department of Mathematical and Computing SciencesGoldsmiths CollegeLondonUK

Personalised recommendations