D-iteration: evaluation of the update algorithm

TL;DR

This paper analyzes the efficiency gains of the D-iteration update algorithm, a fluid diffusion-based iterative method, demonstrating its improved computational performance on evolving graph datasets.

Contribution

It introduces an algebraic representation of D-iteration and evaluates its update algorithm's efficiency through experiments on real datasets.

Findings

D-iteration's update algorithm improves computation efficiency

Fluid diffusion approach effectively handles graph evolution

Experimental results show significant performance gains

Abstract

The aim of this paper is to analyse the gain of the update algorithm associated to the recently proposed D-iteration: the D-iteration is a fluid diffusion based new iterative method. It exploits a simple intuitive decomposition of the product matrix-vector as elementary operations of fluid diffusion (forward scheme) associated to a new algebraic representation. We show through experimentations on real datasets how much this approach can improve the computation efficiency in presence of the graph evolution.