Measuring extremal dependencies in web graphs

Yana Volkovich; Nelly Litvak; Bert Zwart

doi:10.1145/1367497.1367682

Measuring extremal dependencies in web graphs

Yana Volkovich, Nelly Litvak, Bert Zwart

Stochastic Operations Research

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

4 Citations (Scopus)

20 Downloads (Pure)

Abstract

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 language	English
Title of host publication	WWW '08
Subtitle of host publication	Proceedings of the 17th International Conference on the World Wide Web 2008
Place of Publication	New York, NY
Publisher	Association for Computing Machinery
Pages	1113-1114
Number of pages	2
ISBN (Print)	978-1-60558-085-2
DOIs	https://doi.org/10.1145/1367497.1367682
Publication status	Published - Apr 2008
Event	17th International World Wide Web Conference, WWW 2008 - Beijing, China Duration: 21 Apr 2008 → 25 Apr 2008 Conference number: 17

Publication series

Name
Publisher	ACM
Number	WoTUG-31

Conference

Conference	17th International World Wide Web Conference, WWW 2008
Abbreviated title	WWW
Country/Territory	China
City	Beijing
Period	21/04/08 → 25/04/08

Keywords

Regular variation
Web
Wikipedia
Preferential attachment
PageRank

Access to Document

10.1145/1367497.1367682

Cite this

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title = "Measuring extremal dependencies in web graphs",

abstract = "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.",

keywords = "Regular variation, Web, Wikipedia, Preferential attachment, PageRank",

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language = "English",

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TY - GEN

T1 - Measuring extremal dependencies in web graphs

AU - Volkovich, Yana

AU - Litvak, Nelly

AU - Zwart, Bert

N1 - Conference code: 17

PY - 2008/4

Y1 - 2008/4

N2 - 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.

AB - 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.

KW - Regular variation

KW - Web

KW - Wikipedia

KW - Preferential attachment

KW - PageRank

U2 - 10.1145/1367497.1367682

DO - 10.1145/1367497.1367682

M3 - Conference contribution

SN - 978-1-60558-085-2

SP - 1113

EP - 1114

BT - WWW '08

PB - Association for Computing Machinery

CY - New York, NY

T2 - 17th International World Wide Web Conference, WWW 2008

Y2 - 21 April 2008 through 25 April 2008

ER -

Measuring extremal dependencies in web graphs

Abstract

Publication series

Conference

Keywords

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