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A Structural Result for Personalized PageRank and its Algorithmic Consequences

Published:17 December 2019Publication History
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Abstract

Many natural and man-made systems can be represented as graphs, sets of objects (called nodes) and pairwise relations between these objects (called edges). These include the brain, which contains neurons (nodes) that exchange signals through chemical pathways (edges), the Internet, which contains websites (nodes) that are connected via hyperlinks (edges), etc. To study graphs, researchers in diverse domains have used Personalized PageRank (PPR) [6]. Informally, PPR assigns to each node v a vector πv , where πv (w) describes the importance or relevance of node w from the perspective ofv. PPR has proven useful in many applications, both practical and graph-theoretic. Examples include recommending who a user should follow on Twitter [7] (v may wish to follow w if πv (w) is large) and local graph partitioning [2] (the set of nodesw with large πv (w) can be viewed as a community surrounding v).

References

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  • Published in

    cover image ACM SIGMETRICS Performance Evaluation Review
    ACM SIGMETRICS Performance Evaluation Review  Volume 47, Issue 1
    June 2019
    100 pages
    ISSN:0163-5999
    DOI:10.1145/3376930
    Issue’s Table of Contents

    Copyright © 2019 Copyright is held by the owner/author(s)

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 17 December 2019

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