# On the role of clustering in Personalized PageRank estimation

## TL;DR

This paper explores how the clustering structure of graphs can be exploited to improve the efficiency of estimating Personalized PageRank for many node pairs, especially in large networks and distributed computing environments.

## Contribution

It introduces an enhanced PPR estimation algorithm and demonstrates how leveraging clustering reduces computational complexity in both centralized and distributed settings.

## Key findings

- Joint estimation is easier with high clustering.
- Clustering-aware task assignment improves distributed computation efficiency.
- Enhanced Bidirectional-PPR improves multi-pair estimation performance.

## Abstract

Personalized PageRank (PPR) is a measure of the importance of a node from the perspective of another (we call these nodes the $\textit{target}$ and the $\textit{source}$, respectively). PPR has been used in many applications, such as offering a Twitter user (the source) recommendations of who to follow (targets deemed important by PPR); additionally, PPR has been used in graph-theoretic problems such as community detection. However, computing PPR is infeasible for large networks like Twitter, so efficient estimation algorithms are necessary. In this work, we analyze the relationship between PPR estimation complexity and clustering. First, we devise algorithms to estimate PPR for many source/target pairs. In particular, we propose an enhanced version of the existing single pair estimator $\texttt{Bidirectional-PPR}$ that is more useful as a primitive for many pair estimation. We then show that the common underlying graph can be leveraged to efficiently and jointly estimate PPR for many pairs, rather than treating each pair separately using the primitive algorithm. Next, we show the complexity of our joint estimation scheme relates closely to the degree of clustering among the sources and targets at hand, indicating that estimating PPR for many pairs is easier when clustering occurs. Finally, we consider estimating PPR when several machines are available for parallel computation, devising a method that leverages our clustering findings, specifically the quantities computed $\textit{in situ}$, to assign tasks to machines in a manner that reduces computation time. This demonstrates that the relationship between complexity and clustering has important consequences in a practical distributed setting.