Abstract

Abstract. PageRank inherently is massively parallelizable and distributable, as a result of web’s strict host-based link locality. In this paper we show that the Gauß-Seidel iterative method for solving linear systems can be successfully 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 and evaluate 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 information. 1

Citations