PageRank Algorithm using Eigenvector Centrality -- New Approach
This work addresses the need for more efficient centrality measures in network analysis, but it appears incremental as it focuses on substituting one known method with another under specific conditions.
The research tackled the problem of finding an alternative centrality measure to PageRank, concluding that Eigenvector centrality can be safely used in directed networks to improve performance, specifically in terms of time complexity, based on analysis using Spearman's Rank Coefficient Correlation on graphs with many nodes.
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.