Measuring Extremal Dependencies in Web Graphs
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.
Regular variation PageRank Web Wikipedia Preferential attachment
Yana Volkovich Nelly Litvak Bert Zwart
University of Twente P.O. Box 217, 7500 AE Enschede, The Netherlands Georgia Tech.765 Ferst Drive, NW Atlanta,Georgia 30332-0205
国际会议
第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)
北京
英文
2008-04-21（万方平台首次上网日期，不代表论文的发表时间）