Applying weighted PageRank to author citation networks

TL;DR

This study explores the application of weighted PageRank algorithms to author citation networks within Information Retrieval, demonstrating their effectiveness in measuring scholar popularity and prestige, with prestige rank excelling in identifying prize winners.

Contribution

It introduces the use of weighted PageRank with citation and publication data to assess author influence in citation networks, highlighting its effectiveness over traditional measures.

Findings

Weighted PageRank correlates highly with traditional popularity and prestige measures.

Prestige rank outperforms other metrics in identifying IR prize winners.

Both citation and publication weights improve the accuracy of influence measurement.

Abstract

This paper aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information Retrieval (IR) was selected as a test field and data from 1956-2008 were collected from Web of Science (WOS). Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal Component Analysis (PCA) was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures.