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ArticleRank: a PageRank‐based alternative to numbers of citations for analysing citation networks

ArticleRank: a PageRank‐based alternative to numbers of citations for analysing citation networks Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations. Design/methodology/approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database. Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited and ArticleRank is greatest for the most heavily cited articles in a dataset. Originality/value – This is a novel application of the PageRank algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aslib Proceedings: New Information Perspectives Emerald Publishing

ArticleRank: a PageRank‐based alternative to numbers of citations for analysing citation networks

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Publisher
Emerald Publishing
Copyright
Copyright © 2009 Emerald Group Publishing Limited. All rights reserved.
ISSN
0001-253X
DOI
10.1108/00012530911005544
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations. Design/methodology/approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database. Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited and ArticleRank is greatest for the most heavily cited articles in a dataset. Originality/value – This is a novel application of the PageRank algorithm.

Journal

Aslib Proceedings: New Information PerspectivesEmerald Publishing

Published: Nov 13, 2009

Keywords: Bibliographies; Reference services

References