Personalized PageRank with node-dependent restart

K.E. Avrachenkov, R.W. Hofstad, van der, M. Sokol

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

3 Citations (Scopus)

Abstract

Personalized PageRank is an algorithm to classify the importance of web pages on a user-dependent basis. We introduce two generalizations of Personalized PageRank with node-dependent restart. The first generalization is based on the proportion of visits to nodes before the restart, whereas the second generalization is based on the proportion of time a node is visited just before the restart. In the original case of constant restart probability, the two measures coincide. We discuss interesting particular cases of restart probabilities and restart distributions. We show that both generalizations of Personalized PageRank have an elegant expression connecting the so-called direct and reverse Personalized PageRanks that yield a symmetry property of these Personalized PageRanks.
Original languageEnglish
Title of host publicationAlgorithms and Models for the Web Graph (11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014. Proceedings)
EditorsA. Bonato, F.C. Graham, P. Pralat
Place of PublicationBerlin
PublisherSpringer
Pages23-33
ISBN (Print)978-3-319-13122-1
DOIs
Publication statusPublished - 2014
Eventconference; 11th International Workshop on Algorithms and Models for the Web Graph; 2014-12-17; 2014-12-18 -
Duration: 17 Dec 201418 Dec 2014

Publication series

NameLecture Notes in Computer Science
Volume8882
ISSN (Print)0302-9743

Conference

Conferenceconference; 11th International Workshop on Algorithms and Models for the Web Graph; 2014-12-17; 2014-12-18
Period17/12/1418/12/14
Other11th International Workshop on Algorithms and Models for the Web Graph

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  • Cite this

    Avrachenkov, K. E., Hofstad, van der, R. W., & Sokol, M. (2014). Personalized PageRank with node-dependent restart. In A. Bonato, F. C. Graham, & P. Pralat (Eds.), Algorithms and Models for the Web Graph (11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014. Proceedings) (pp. 23-33). (Lecture Notes in Computer Science; Vol. 8882). Springer. https://doi.org/10.1007/978-3-319-13123-8_3