The Juxtaposed approximate PageRank method for robust PageRank approximation in a peer-to-peer web search network

The Juxtaposed approximate PageRank method for robust PageRank approximation in a peer-to-peer... We present Juxtaposed approximate PageRank (JXP), a distributed algorithm for computing PageRank-style authority scores of Web pages on a peer-to-peer (P2P) network. Unlike previous algorithms, JXP allows peers to have overlapping content and requires no a priori knowledge of other peers’ content. Our algorithm combines locally computed authority scores with information obtained from other peers by means of random meetings among the peers in the network. This computation is based on a Markov-chain state-lumping technique, and iteratively approximates global authority scores. The algorithm scales with the number of peers in the network and we show that the JXP scores converge to the true PageRank scores that one would obtain with a centralized algorithm. Finally, we show how to deal with misbehaving peers by extending JXP with a reputation model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

The Juxtaposed approximate PageRank method for robust PageRank approximation in a peer-to-peer web search network

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Publisher
Springer-Verlag
Copyright
Copyright © 2008 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-007-0057-y
Publisher site
See Article on Publisher Site

Abstract

We present Juxtaposed approximate PageRank (JXP), a distributed algorithm for computing PageRank-style authority scores of Web pages on a peer-to-peer (P2P) network. Unlike previous algorithms, JXP allows peers to have overlapping content and requires no a priori knowledge of other peers’ content. Our algorithm combines locally computed authority scores with information obtained from other peers by means of random meetings among the peers in the network. This computation is based on a Markov-chain state-lumping technique, and iteratively approximates global authority scores. The algorithm scales with the number of peers in the network and we show that the JXP scores converge to the true PageRank scores that one would obtain with a centralized algorithm. Finally, we show how to deal with misbehaving peers by extending JXP with a reputation model.

Journal

The VLDB JournalSpringer Journals

Published: Mar 1, 2008

References

  • A non-manipulable trust system based on eigentrust
    Abrams, Z.; McGrew, R.; Plotkin, S.
  • Space/time trade-offs in hash coding with allowable errors
    Bloom, B.H.
  • Link analysis ranking: algorithms, theory, and experiments
    Borodin, A.; Roberts, G.O.; Rosenthal, J.S.; Tsaparas, P.

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