Instance-Optimality in PageRank Computation

TL;DR

This paper proves that a bidirectional algorithm for estimating PageRank is nearly optimal for certain classes of graphs, establishing fundamental limits and extending results to weighted and multigraphs.

Contribution

It introduces an instance-optimality result for a bidirectional PageRank algorithm on specific graph classes, and explores its limitations and extensions.

Findings

The algorithm is instance-optimal up to polylogarithmic factors for graphs with bounded degrees.

Counterexample shows the algorithm is not optimal for graphs with degrees close to n.

The algorithm is instance-optimal on all multigraphs, but has limitations on weighted simple graphs.

Abstract

We study the problem of estimating a vertex's PageRank within a constant relative error, with constant probability. We prove that an adaptive variant of the simple classic bidirectional algorithm is instance-optimal up to a polylogarithmic factor for all directed graphs of order $n$ whose maximum in- and out-degrees are at most a constant fraction of $n$. In other words, there is no correct algorithm that can be faster than our algorithm on any such graph by more than a polylogarithmic factor. We further extend the instance-optimality to all graphs in which at most a polylogarithmic number of vertices have unbounded degrees. This covers all sparse graphs with $\tilde{O}(n)$ edges. In addition, we provide a counterexample showing that the bidirectional algorithm is not instance-optimal for graphs whose degrees are mostly equal to $n$. We also consider weighted graphs and multigraphs. We show that the bidirectional algorithm is instance-optimal on \emph{all} multigraphs, but for weighted simple graphs, we have almost the same limitations as for unweighted simple graphs.