Personalized PageRank Estimation in Undirected Graphs

TL;DR

This paper provides a comprehensive analysis of the time complexity for estimating Personalized PageRank in undirected graphs, establishing tight bounds and introducing new techniques that leverage the undirected structure.

Contribution

It offers the first complete characterization of PPR estimation in undirected graphs, with tight bounds and novel algorithms that exploit undirected graph properties.

Findings

Established tight bounds for PPR estimation in undirected graphs.

Developed new algorithms leveraging undirected graph structure.

Provided both worst-case and average-case analyses.

Abstract

Given an undirected graph $G=(V, E)$, the Personalized PageRank (PPR) of $t\in V$ with respect to $s\in V$, denoted $\pi(s,t)$, is the probability that an $\alpha$-discounted random walk starting at $s$ terminates at $t$. We study the time complexity of estimating $\pi(s,t)$ with constant relative error and constant failure probability, whenever $\pi(s,t)$ is above a given threshold parameter $\delta\in(0,1)$. We consider common graph-access models and furthermore study the single source, single target, and single node (PageRank centrality) variants of the problem. We provide a complete characterization of PPR estimation in undirected graphs by giving tight bounds (up to logarithmic factors) for all problems and model variants in both the worst-case and average-case setting. This includes both new upper and lower bounds. Tight bounds were recently obtained by Bertram, Jensen, Thorup, Wang, and Yan for directed graphs. However, their lower bound constructions rely on asymmetry and therefore do not carry over to undirected graphs. At the same time, undirected graphs exhibit additional structure that can be exploited algorithmically. Our results resolve the undirected case by developing new techniques that capture both aspects, yielding tight bounds.