D-Iteration: diffusion approach for solving PageRank

TL;DR

The paper introduces D-Iteration, a fluid diffusion-based method that accelerates PageRank computation, demonstrating improved efficiency over traditional algorithms through theoretical analysis and real Web graph experiments.

Contribution

It presents a novel diffusion-based algorithm for faster PageRank computation with proven convergence and practical efficiency gains.

Findings

D-Iteration outperforms Power Iteration, Gauss-Seidel, and OPIC in speed.

The method is adaptable to asynchronous implementations.

Theoretical convergence guarantees are provided.

Abstract

In this paper we present a new method that can accelerate the computation of the PageRank importance vector. Our method, called D-Iteration (DI), is based on the decomposition of the matrix-vector product that can be seen as a fluid diffusion model and is potentially adapted to asynchronous implementation. We give theoretical results about the convergence of our algorithm and we show through experimentations on a real Web graph that DI can improve the computation efficiency compared to other classical algorithm like Power Iteration, Gauss-Seidel or OPIC.