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  Efficient and Decentralized PageRank Approximation in a Peer-to-Peer Web Search Network

Parreira, J. X., Donato, D., Michel, S., & Weikum, G. (2006). Efficient and Decentralized PageRank Approximation in a Peer-to-Peer Web Search Network. In Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB 2006) (pp. 415-426). New York, USA: ACM.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-22A7-6 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-22A8-4
Genre: Conference Paper

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 Creators:
Parreira, Josiane Xavier1, Author           
Donato, Debora, Author
Michel, Sebastian1, Author           
Weikum, Gerhard1, Author           
Dayal, Umeshwar, Editor
Whang, Kyu-Young, Editor
Lomet, David B., Editor
Alonso, Gustavo, Editor
Lohman, Guy M., Editor
Kersten, Martin L., Editor
Cha, Sang Kyun, Editor
Kim, Young-Kuk, Editor
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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 Abstract: PageRank-style (PR) link analyses are a cornerstone of Web search engines and Web mining, but they are computationally expensive. Recently, various techniques have been proposed for speeding up these analyses by distributing the link graph among multiple sites. However, none of these advanced methods is suitable for a fully decentralized PR computation in a peer-to-peer (P2P) network with autonomous peers, where each peer can independently crawl Web fragments according to the user's thematic interests. In such a setting the graph fragments that different peers have locally available or know about may arbitrarily overlap among peers, creating additional complexity for the PR computation. This paper presents the JXP algorithm for dynamically and collaboratively computing PR scores of Web pages that are arbitrarily distributed in a P2P network. The algorithm runs at every peer, and it works by combining locally computed PR scores with random meetings among the peers in the network. It is scalable as the number of peers on the network grows, and experiments as well as theoretical arguments show that JXP scores converge to the true PR scores that one would obtain by a centralized computation.

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Language(s): eng - English
 Dates: 2007-04-102006
 Publication Status: Issued
 Pages: -
 Publishing info: New York, USA : ACM
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 314656
Other: Local-ID: C1256DBF005F876D-612CD17494FD224DC125718000419F51-ParreiraDMW06
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Title: Untitled Event
Place of Event: Seoul, Korea
Start-/End Date: 2006-09-12

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Title: Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB 2006)
Source Genre: Proceedings
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Publ. Info: New York, USA : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 415 - 426 Identifier: ISBN: 1-59593-385-9