# Distributed Algorithms for Fully Personalized PageRank on Large Graphs

## TL;DR

This paper introduces a distributed framework for efficiently computing fully edge-weighted Personalized PageRank on large graphs, using Monte Carlo methods and several optimizations to significantly outperform existing approaches.

## Contribution

It presents a novel distributed algorithm employing Monte Carlo approximation and optimization techniques for large-scale, weighted PPR computation, addressing scalability and accuracy issues.

## Key findings

- Several orders of magnitude faster than state-of-the-art methods.
- Largely improves accuracy over baseline algorithms.
- Effectively handles graphs with billions of edges.

## Abstract

Personalized PageRank (PPR) has enormous applications, such as link prediction and recommendation systems for social networks, which often require the fully PPR to be known. Besides, most of real-life graphs are edge-weighted, e.g., the interaction between users on the Facebook network. However, it is computationally difficult to compute the fully PPR, especially on large graphs, not to mention that most existing approaches do not consider the weights of edges. In particular, the existing approach cannot handle graphs with billion edges on a moderate-size cluster. To address this problem, this paper presents a novel study on the computation of fully edge-weighted PPR on large graphs using the distributed computing framework. Specifically, we employ the Monte Carlo approximation that performs a large number of random walks from each node of the graph, and exploits the parallel pipeline framework to reduce the overall running time of the fully PPR. Based on that, we develop several optimization techniques which (i) alleviate the issue of large nodes that could explode the memory space, (ii) pre-compute short walks for small nodes that largely speedup the computation of random walks, and (iii) optimize the amount of random walks to compute in each pipeline that significantly reduces the overhead. With extensive experiments on a variety of real-life graph datasets, we demonstrate that our solution is several orders of magnitude faster than the state-of-the-arts, and meanwhile, largely outperforms the baseline algorithms in terms of accuracy.