Distributed Randomized Algorithms for the PageRank Computation

TL;DR

This paper introduces distributed randomized algorithms for computing PageRank, enabling web pages to locally update importance scores through link-based communication, with proven convergence to true values.

Contribution

It presents novel distributed randomized schemes for PageRank computation with proven mean-square convergence, connecting to multi-agent consensus problems.

Findings

Algorithms asymptotically converge to true PageRank values

Distributed schemes enable local updates via link communication

The approach relates to multi-agent consensus problems

Abstract

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.