PageRank Algorithm using Eigenvector Centrality -- New Approach

TL;DR

This paper proposes using Eigenvector centrality as an alternative to PageRank in directed networks, demonstrating comparable effectiveness and improved computational efficiency through correlation analysis.

Contribution

It introduces a new approach for replacing PageRank with Eigenvector centrality in directed networks, supported by correlation analysis and performance evaluation.

Findings

Eigenvector centrality correlates well with PageRank in directed networks

Using Eigenvector can reduce computational time compared to PageRank

The approach is suitable for large-scale directed graph analysis

Abstract

The purpose of the research is to find a centrality measure that can be used in place of PageRank and to find out the conditions where we can use it in place of PageRank. After analysis and comparison of graphs with a large number of nodes using Spearman's Rank Coefficient Correlation, the conclusion is evident that Eigenvector can be safely used in place of PageRank in directed networks to improve the performance in terms of the time complexity.