Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Advances in Information Retrieval |
| Untertitel | 28th European Conference on IR Research, ECIR 2006, Proceedings |
| Herausgeber (Verlag) | Springer Verlag |
| Seiten | 241-252 |
| Seitenumfang | 12 |
| ISBN (elektronisch) | 978-3-540-33348-7 |
| ISBN (Print) | 978-3-540-33347-0 |
| Publikationsstatus | Veröffentlicht - 2006 |
| Veranstaltung | 28th European Conference on Information Retrieval Research, ECIR 2006 - London, Großbritannien / Vereinigtes Königreich Dauer: 10 Apr. 2006 → 12 Apr. 2006 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 3936 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
PageRank inherently is massively parallelizable and distributable, as a result of web's strict host-based link locality, We show that the Gau-Seidel iterative method can actually be applied in such a parallel ranking scenario in order to improve convergence. By introducing a two-dimensional web model and by adapting the PageRank to this environment, we present efficient methods to compute the exact rank vector even for large-scale web graphs in only a few minutes and iteration steps, with intrinsic support for incremental web crawling, and without the need for page sorting/reordering or for sharing global rank information.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Advances in Information Retrieval: 28th European Conference on IR Research, ECIR 2006, Proceedings. Springer Verlag, 2006. S. 241-252 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 3936 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Efficient parallel computation of pageRank
AU - Kohlschütter, Christian
AU - Chirita, Paul Alexandru
AU - Nejdl, Wolfgang
PY - 2006
Y1 - 2006
N2 - PageRank inherently is massively parallelizable and distributable, as a result of web's strict host-based link locality, We show that the Gau-Seidel iterative method can actually be applied in such a parallel ranking scenario in order to improve convergence. By introducing a two-dimensional web model and by adapting the PageRank to this environment, we present efficient methods to compute the exact rank vector even for large-scale web graphs in only a few minutes and iteration steps, with intrinsic support for incremental web crawling, and without the need for page sorting/reordering or for sharing global rank information.
AB - PageRank inherently is massively parallelizable and distributable, as a result of web's strict host-based link locality, We show that the Gau-Seidel iterative method can actually be applied in such a parallel ranking scenario in order to improve convergence. By introducing a two-dimensional web model and by adapting the PageRank to this environment, we present efficient methods to compute the exact rank vector even for large-scale web graphs in only a few minutes and iteration steps, with intrinsic support for incremental web crawling, and without the need for page sorting/reordering or for sharing global rank information.
UR - http://www.scopus.com/inward/record.url?scp=33745841483&partnerID=8YFLogxK
U2 - 10.1007/11735106_22
DO - 10.1007/11735106_22
M3 - Conference contribution
AN - SCOPUS:33745841483
SN - 978-3-540-33347-0
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 241
EP - 252
BT - Advances in Information Retrieval
PB - Springer Verlag
T2 - 28th European Conference on Information Retrieval Research, ECIR 2006
Y2 - 10 April 2006 through 12 April 2006
ER -