An Optimization Ranking Approach Based on Weighted Citation Networks and P-Rank Algorithm

Abstract

Evaluating scientific articles has always been a challenging task, made even more difficult by the constantly evolving citation networks. Despite numerous attempts at solving this problem, most existing approaches fail to consider the link relationships within the citation network, which can often result in biased evaluation results. To overcome this limitation, we present an optimization ranking algorithm that leverages the P-Rank algorithm and weighted citation networks to provide a more accurate article ranking. The proposed approach employs two hyperbolic tangent functions to calculate the corresponding age of articles and the number of citations, while also updating the link relationships of each paper node in the citation network. We validate the effectiveness of the proposed approach using three evaluation indicators and conduct experiments on three public datasets. The obtained experimental results demonstrate that the optimization article ranking method can achieve competitive performance when compared to other unweighted ranking algorithms. In addition, we note that the optimal Spearman’s rank correlation and robustness can all be achieved by using a combination of the following parameters: α = 10, β = 5, and γ = 2.