PageRank for ranking authors in co-citation networks
Ying Ding, Erjia Yan, Arthur Frazho, James Caverlee

TL;DR
This paper explores how varying damping factors in PageRank and introducing weighted PageRank algorithms can enhance author ranking in co-citation networks, providing new insights into research impact assessment.
Contribution
It introduces weighted PageRank algorithms and analyzes the effect of damping factors on author rankings in co-citation networks, which is a novel approach in bibliometrics.
Findings
Citation rank correlates highly with PageRank across damping factors.
PageRank results are not significantly correlated with centrality measures.
h-index shows no significant correlation with centrality measures.
Abstract
Google's PageRank has created a new synergy to information retrieval for a better ranking of Web pages. It ranks documents depending on the topology of the graphs and the weights of the nodes. PageRank has significantly advanced the field of information retrieval and keeps Google ahead of competitors in the search engine market. It has been deployed in bibliometrics to evaluate research impact, yet few of these studies focus on the important impact of the damping factor (d) for ranking purposes. This paper studies how varied damping factors in the PageRank algorithm can provide additional insight into the ranking of authors in an author co-citation network. Furthermore, we propose weighted PageRank algorithms. We select 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculate the ranks of these 108…
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Taxonomy
TopicsComplex Network Analysis Techniques · Web visibility and informetrics
