PageRank Computation via Web Aggregation in Distributed Randomized Algorithms

PageRank is one of the many measures that the search engine at Google employs to rate the popularity and importance of each page in the web. In this paper, we present extensions of the distributed algorithms which we recently proposed for the computation of PageRank. By distributed, we mean that each page computes its own PageRank value by interacting with linked pages. While our original algorithms relied on gossip-type randomization for choosing pages to make updates, here we pursue a more general deterministic approach. It is then modified for aggregation-based computation by grouping pages in the same domain. Through numerical simulations using real web data, we demonstrate the fast convergence of our algorithms in comparison with other techniques.