Multilinear PageRank

TL;DR

This paper extends PageRank to higher-order Markov chains using multilinear tensor methods, analyzes convergence properties, and identifies parameter regimes with non-unique solutions and convergence issues.

Contribution

It introduces multilinear PageRank as a computationally feasible approximation and develops convergence theory for associated fixed-point and Newton methods.

Findings

Convergence of fixed-point, shifted fixed-point, and Newton methods is established in certain regimes.

Non-uniqueness and non-convergence occur in specific parameter regimes.

A repository of non-convergent cases is provided for future research.

Abstract

In this paper, we first extend the celebrated PageRank modification to a higher-order Markov chain. Although this system has attractive theoretical properties, it is computationally intractable for many interesting problems. We next study a computationally tractable approximation to the higher-order PageRank vector that involves a system of polynomial equations called multilinear PageRank, which is a type of tensor PageRank vector. It is motivated by a novel "spacey random surfer" model, where the surfer remembers bits and pieces of history and is influenced by this information. The underlying stochastic process is an instance of a vertex-reinforced random walk. We develop convergence theory for a simple fixed-point method, a shifted fixed-point method, and a Newton iteration in a particular parameter regime. In marked contrast to the case of the PageRank vector of a Markov chain where the solution is always unique and easy to compute, there are parameter regimes of multilinear PageRank where solutions are not unique and simple algorithms do not converge. We provide a repository of these non-convergent cases that we encountered through exhaustive enumeration and randomly sampling that we believe is useful for future study of the problem.