A Web Aggregation Approach for Distributed Randomized PageRank Algorithms

TL;DR

This paper introduces a web aggregation method to improve distributed randomized PageRank algorithms by reducing computational and communication loads through systematic grouping and aggregation of web pages.

Contribution

It develops a novel aggregation approach exploiting web sparsity to enhance distributed PageRank computation efficiency with proven convergence properties.

Findings

Reduces computation and communication loads in distributed PageRank algorithms.

Provides a systematic web page grouping method based on sparsity.

Ensures convergence of the aggregated PageRank update scheme.

Abstract

The PageRank algorithm employed at Google assigns a measure of importance to each web page for rankings in search results. In our recent papers, we have proposed a distributed randomized approach for this algorithm, where web pages are treated as agents computing their own PageRank by communicating with linked pages. This paper builds upon this approach to reduce the computation and communication loads for the algorithms. In particular, we develop a method to systematically aggregate the web pages into groups by exploiting the sparsity inherent in the web. For each group, an aggregated PageRank value is computed, which can then be distributed among the group members. We provide a distributed update scheme for the aggregated PageRank along with an analysis on its convergence properties. The method is especially motivated by results on singular perturbation techniques for large-scale Markov chains and multi-agent consensus.