The PageRank Problem, Multi-Agent Consensus and Web Aggregation -- A Systems and Control Viewpoint

TL;DR

This paper reviews the PageRank algorithm, its connection to multi-agent consensus, and web aggregation, presenting a systems and control perspective on distributed ranking methods and related problems.

Contribution

It introduces a distributed randomized approach to PageRank using Markov chain techniques and explores its links to multi-agent consensus and aggregation methods.

Findings

Proposes a Markov chain-based distributed algorithm for PageRank.

Establishes connections between PageRank, multi-agent consensus, and control systems.

Highlights applications in information technology and network ranking.

Abstract

PageRank is an algorithm introduced in 1998 and used by the Google Internet search engine. It assigns a numerical value to each element of a set of hyperlinked documents (that is, web pages) within the World Wide Web with the purpose of measuring the relative importance of the page. The key idea in the algorithm is to give a higher PageRank value to web pages which are visited often by web surfers. On its website, Google describes PageRank as follows: ``PageRank reflects our view of the importance of web pages by considering more than 500 million variables and 2 billion terms. Pages that are considered important receive a higher PageRank and are more likely to appear at the top of the search results." Today PageRank is a paradigmatic problem of great interest in various areas, such as information technology, bibliometrics, biology, and e-commerce, where objects are often ranked in order of importance. This article considers a distributed randomized approach based on techniques from the area of Markov chains using a graph representation consisting of nodes and links. We also outline connections with other problems of current interest to the systems and control community, which include ranking of control journals, consensus of multi-agent systems, and aggregation-based techniques.