Measuring extremal dependencies in web graphs

Yana Volkovich, Nelly Litvak, Bert Zwart

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
9 Downloads (Pure)


We analyze dependencies in power law graph data (Web sample, Wikipedia sample and a preferential attachment graph) using statistical inference for multivariate regular variation. The well developed theory of regular variation is widely applied in extreme value theory, telecommunications and mathematical finance, and it provides a natural mathematical formalism for analyzing dependencies between variables with power laws. However, most of the proposed methods have never been used in the Web graph data mining. The present work fills this gap. The new insights this yields are striking: the three above-mentioned data sets are shown to have a totally different dependence structure between different graph parameters, such as in-degree and PageRank.
Original languageEnglish
Title of host publicationWWW '08
Subtitle of host publicationProceedings of the 17th International Conference on the World Wide Web 2008
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Number of pages2
ISBN (Print)978-1-60558-085-2
Publication statusPublished - Apr 2008
Event17th International World Wide Web Conference, WWW 2008 - Beijing, China
Duration: 21 Apr 200825 Apr 2008
Conference number: 17

Publication series



Conference17th International World Wide Web Conference, WWW 2008
Abbreviated titleWWW


  • Regular variation
  • Web
  • Wikipedia
  • Preferential attachment
  • PageRank

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