Abstract
PageRank inherently is massively parallelizable and distributable, as a result of web’s strict host-based link locality. We show that the Gauß-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.
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Kohlschütter, C., Chirita, PA., Nejdl, W. (2006). Efficient Parallel Computation of PageRank. In: Lalmas, M., MacFarlane, A., Rüger, S., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds) Advances in Information Retrieval. ECIR 2006. Lecture Notes in Computer Science, vol 3936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11735106_22
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DOI: https://doi.org/10.1007/11735106_22
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