  In the search engine of Google, the PageRank algorithm plays a crucial role
in ranking the search results. The algorithm quantifies the importance of each
web page based on the link structure of the web. We first provide an overview
of the original problem setup. Then, we propose several distributed randomized
schemes for the computation of the PageRank, where the pages can locally update
their values by communicating to those connected by links. The main objective
of the paper is to show that these schemes asymptotically converge in the
mean-square sense to the true PageRank values. A detailed discussion on the
close relations to the multi-agent consensus problems is also given.
