Personalized PageRank to a Target Node, Revisited

TL;DR

This paper introduces RBS, an efficient algorithm for single-target Personalized PageRank queries, which improves accuracy and speed in applications like heavy hitters detection, SimRank computation, and graph neural networks.

Contribution

The paper presents RBS, a novel algorithm for approximate single-target PPR queries with optimal complexity, addressing a less-studied direction of importance in graph analysis.

Findings

RBS outperforms existing algorithms in efficiency and precision.

RBS effectively improves heavy hitters PPR query accuracy.

RBS enhances scalability for graph neural network applications.

Abstract

Personalized PageRank (PPR) is a widely used node proximity measure in graph mining and network analysis. Given a source node $s$ and a target node $t$, the PPR value $\pi(s,t)$ represents the probability that a random walk from $s$ terminates at $t$, and thus indicates the bidirectional importance between $s$ and $t$. The majority of the existing work focuses on the single-source queries, which asks for the PPR value of a given source node $s$ and every node $t \in V$. However, the single-source query only reflects the importance of each node $t$ with respect to $s$. In this paper, we consider the {\em single-target PPR query}, which measures the opposite direction of importance for PPR. Given a target node $t$, the single-target PPR query asks for the PPR value of every node $s\in V$ to a given target node $t$. We propose RBS, a novel algorithm that answers approximate single-target queries with optimal computational complexity. We show that RBS improves three concrete applications: heavy hitters PPR query, single-source SimRank computation, and scalable graph neural networks. We conduct experiments to demonstrate that RBS outperforms the state-of-the-art algorithms in terms of both efficiency and precision on real-world benchmark datasets.