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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Kohlschütter, C., Chirita, P. A., & Nejdl, W. (2006). Efficient parallel computation of pageRank. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3936 LNCS, pp. 241–252). Springer Verlag. https://doi.org/10.1007/11735106_22
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