Two Parallel PageRank Algorithms via Improving Forward Push

TL;DR

This paper introduces two parallel algorithms, IFP1 and IFP2, that improve the efficiency of PageRank computation by leveraging graph structure and reducing computation, achieving significant speedups over traditional methods.

Contribution

The paper proposes two novel parallel PageRank algorithms, IFP1 and IFP2, that enhance convergence and reduce computation compared to existing methods.

Findings

IFP2 with 38 parallelism is up to 50 times faster than the Power method.

Both IFP1 and IFP2 outperform the Power method on six data sets.

IFP1 leverages DAG structure and dangling vertices to improve convergence.

Abstract

Initially used to rank web pages, PageRank has now been applied in many fields. With the growing scale of graph, accelerating PageRank computing is urged and designing parallel algorithm is a feasible solution. In this paper, two parallel PageRank algorithms IFP1 and IFP2 are proposed via improving the state-of-the-art Personalized PageRank algorithm, i.e., Forward Push. Theoretical analysis indicates that, IFP1 can take advantage of the DAG structure of the graph, where the dangling vertices improves the convergence rate and the unreferenced vertices decreases the computation amount. As an improvement of IFP1, IFP2 pushes mass to the dangling vertices only once but rather many times, and thus decreases the computation amount further. Experiments on six data sets illustrate that both IFP1 and IFP2 outperform Power method, where IFP2 with 38 parallelism can be at most 50 times as fast as the Power method.