An Incrementally Expanding Approach for Updating PageRank on Dynamic Graphs

TL;DR

This paper introduces the Dynamic Frontier approach for efficiently updating PageRank scores on large, dynamic graphs, significantly outperforming existing methods through parallel processing and incremental updates.

Contribution

It presents a novel incremental algorithm that efficiently updates PageRank on dynamic graphs with minimal overhead and high parallel scalability.

Findings

Outperforms static and naive dynamic methods by up to 7.8x

Achieves 1.8x speedup with each doubling of threads

Effective on large-scale, randomly updated graphs

Abstract

PageRank is a popular centrality metric that assigns importance to the vertices of a graph based on its neighbors and their score. Efficient parallel algorithms for updating PageRank on dynamic graphs is crucial for various applications, especially as dataset sizes have reached substantial scales. This technical report presents our Dynamic Frontier approach. Given a batch update of edge deletion and insertions, it progressively identifies affected vertices that are likely to change their ranks with minimal overhead. On a server equipped with a 64-core AMD EPYC-7742 processor, our Dynamic Frontier PageRank outperforms Static, Naive-dynamic, and Dynamic Traversal PageRank by 7.8x, 2.9x, and 3.9x respectively - on uniformly random batch updates of size 10^-7 |E| to 10^-3 |E|. In addition, our approach improves performance at an average rate of 1.8x for every doubling of threads.