Weighted P-Rank: a Weighted Article Ranking Algorithm Based on a Heterogeneous Scholarly Network

The evaluation and ranking of scientific article have always been a very challenging task because of the dynamic change of citation networks. Over the past decades, plenty of studies have been conducted on this topic. However, most of the current methods do not consider the link weightings between different networks, which might lead to biased article ranking results. To tackle this issue, we develop a weighted P-Rank algorithm based on a heterogeneous scholarly network for article ranking evaluation. In this study, the corresponding link weightings in heterogeneous scholarly network can be updated by calculating citation relevance, authors’ contribution, and journals’ impact. To further boost the performance, we also employ the time information of each article as a personalized PageRank vector to balance the bias to earlier publications in the dynamic citation network. The experiments are conducted on three public datasets (arXiv, Cora, and MAG). The experimental results demonstrated that weighted P-Rank algorithm significantly outperforms other ranking algorithms on arXiv and MAG datasets, while it achieves competitive performance on Cora dataset. Under different network configuration conditions, it can be found that the best ranking result can be obtained by jointly utilizing all kinds of weighted information.