  The PageRank algorithm employed by Google quantifies the importance of each
page by the link structure of the web. To reduce the computational burden the
distributed randomized PageRank algorithms (DRPA) recently appeared in
literature suggest pages to update their ranking values by locally
communicating with the linked pages. The main objective of the note is to show
that the estimates generated by DRPA converge to the true PageRank value almost
surely under the assumption that the randomization is realized in an
independent and identically distributed (iid) way. This is achieved with the
help of the stochastic approximation (SA) and its convergence results.
