Analysing ranking algorithms and publication trends on scholarly citation networks

[摘要] ENGLISH ABSTRACT: Citation analysis is an important tool in the academic community. It can aid universities,funding bodies, and individual researchers to evaluate scientific work and direct resourcesappropriately. With the rapid growth of the scientific enterprise and the increase of onlinelibraries that include citation analysis tools, the need for a systematic evaluation of thesetools becomes more important.The research presented in this study deals with scientific research output, i.e., articlesand citations, and how they can be used in bibliometrics to measure academic success.More specifically, this research analyses algorithms that rank academic entities such asarticles, authors and journals to address the question of how well these algorithms canidentify important and high-impact entities.A consistent mathematical formulation is developed on the basis of a categorisationof bibliometric measures such as the h-index, the Impact Factor for journals, and rankingalgorithms based on Google's PageRank. Furthermore, the theoretical properties of eachalgorithm are laid out.The ranking algorithms and bibliometric methods are computed on the MicrosoftAcademic Search citation database which contains 40 million papers and over 260 millioncitations that span across multiple academic disciplines.We evaluate the ranking algorithms by using a large test data set of papers and authorsthat won renowned prizes at numerous Computer Science conferences. The results showthat using citation counts is, in general, the best ranking metric. However, for certaintasks, such as ranking important papers or identifying high-impact authors, algorithmsbased on PageRank perform better. As a secondary outcome of this research, publicationtrends across academic disciplines are analysed to show changes in publication behaviourover time and differences in publication patterns between disciplines.

[发布机构] Stellenbosch University