引文分析中，以被引次数作为测度指标，常常出现大量相同的被引次数（tie），例如，两篇论文都被引用了50次。我们认为，不同级别学者施引的份量（权重）是不同的。但当前引文分析中，统计被引次数时大牛的引用与新手的引用被视为等同，将不同级别学者的引用做几何累加。“被引次数”、“期刊影响因子”、“即年指标”、“h指数”等指标无不如此。为了合理地给不同的施引赋予不同的权重，我们改进Google的PageRank算法，提出ArticleRank算法。与“被引次数”这一指标相比，该算法能有效地区分不同级别学者的引用，尤其适合测度高被引论文的学术影响力。

       论文发表在Aslib Proceedings（SSCI收录），附论文标题与摘要：

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

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.

Keywords – Bibliographies, Reference services

Paper type – Research paper