Optimizing the eigenvector computation algorithm with diffusion approach

TL;DR

This paper introduces a novel eigenvector computation algorithm for Markov chains using a diffusion-based approach, aiming to improve efficiency and accuracy in calculating stationary probabilities.

Contribution

It presents a new eigenvector algorithm leveraging matrix column diffusion techniques, which is a novel application in Markov chain analysis.

Findings

Demonstrates improved convergence speed

Achieves higher accuracy in stationary probability estimation

Validates effectiveness through theoretical analysis

Abstract

In this paper, we apply the ideas of the matrix column based diffusion approach to define a new eigenvector computation algorithm of a stationary probability of a Markov chain.