title: Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods.
abstract: Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.
authors.affiliation: University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia.
authors.forename: Lovro
authors.initials: L
authors.lastname: Šubelj
authors.affiliation: Leiden University, Centre for Science and Technology Studies, Leiden, Netherlands.
authors.forename: Nees Jan
authors.initials: NJ
authors.lastname: van Eck
authors.affiliation: Leiden University, Centre for Science and Technology Studies, Leiden, Netherlands.
authors.forename: Ludo
authors.initials: L
authors.lastname: Waltman
mesh_terms: D015706:Bibliometrics; D016000:Cluster Analysis; D006801:Humans; D010506:Periodicals as Topic; D012586:Science / Q000706:statistics & numerical data*