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

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45166

Parreira,  Josiane Xavier
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45041

Michel,  Sebastian
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

Parreira, J. X., Castillo, C., Donato, D., Michel, S., & Weikum, G. (2008). The Juxtaposed approximate PageRank method for robust PageRank approximation in a peer-to-peer web search network. VLDB Journal, 17(2), 291-313. doi:10.1007/s00778-007-0057-y.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1D2A-9
Zusammenfassung
We present Juxtaposed approximate PageRank ({JXP}), a distributed algorithm for computing PageRank-style authority scores of Web pages on a peer-to-peer ({P}2{P}) 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.