Distributed randomized PageRank computation based on web aggregation

The so-called PageRank algorithm has been used at Google for properly ranking search results. It quantifies the importance of each page by the structure of links in the web. In our recent work, we have proposed a distributed randomized approach for the PageRank computation, where the pages find their own values by communicating with linked pages. This paper builds upon this approach to improve the computation and communication load for the algorithm by systematically aggregating the Web pages while maintaining performance.