Clustering scientific publications based on citation relations: A systematic comparison of different methods

TL;DR

This paper systematically compares various citation-based clustering methods for scientific publications, evaluating their performance on different networks and through expert assessment, highlighting the effectiveness of map equation methods.

Contribution

It provides a comprehensive comparison of clustering techniques in citation networks, emphasizing the superior performance of map equation methods in bibliometric analysis.

Findings

Map equation methods perform best overall.

Trade-offs exist between different clustering properties.

Expert assessment supports the quantitative results.

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.