Abstract
The effects of enhancing direct citations, with respect to publication–publication relatedness measurement, by indirect citation relations (bibliographic coupling, cocitation, and extended direct citations) and text relations on clustering solution accuracy are analyzed. For comparison, we include each approach that is involved in the enhancement of direct citations. In total, we investigate the relative performance of seven approaches. To evaluate the approaches we use a methodology proposed by earlier research. However, the evaluation criterion used is based on MeSH, one of the most sophisticated publication-level classification schemes available. We also introduce an approach, based on interpolated accuracy values, by which overall relative clustering solution accuracy can be studied. The results show that the cocitation approach has the worst performance, and that the direct citations approach is outperformed by the other five investigated approaches. The extended direct citations approach has the best performance, followed by an approach in which direct citations are enhanced by the BM25 textual relatedness measure. An approach that combines direct citations with bibliographic coupling and cocitation performs slightly better than the bibliographic coupling approach, which in turn has a better performance than the BM25 approach.
Highlights
Community detection in citation networks, which is the topic of this paper, can be performed in order to analyze both the obvious and the more subtle relations between scientific publications, as well as the identification of subfields of science (e.g., Chen & Redner, 2010; Klavans & Boyack, 2017; Waltman & Van Eck, 2012)
We have analyzed the effects of enhancing direct citations, with respect to publication– publication relatedness measurement, by indirect citation relations and text relations on clustering solution accuracy
We used an approach based on Medical Subject Headings (MeSH), one of the most sophisticated publication-level classification schemes available, as the independent evaluation criterion
Summary
Community detection in citation networks, which is the topic of this paper, can be performed in order to analyze both the obvious and the more subtle relations between scientific publications, as well as the identification of subfields of science (e.g., Chen & Redner, 2010; Klavans & Boyack, 2017; Waltman & Van Eck, 2012). In the context of networks, communities are clusters of closely connected nodes within a network. Communities of this kind are found in citation networks, and in many other networks, such as biological networks, the World Wide Web, social networks, and collaboration works (Girvan & Newman, 2002). Enhancing direct citations with a given topic tend to cite similar publications with respect to topic. Communities in a citation network thereby contain similar publications regarding a single topic or a set of related topics. Community detection in a citation network can be used to uncover related publications. The detected subfields, and interrelations between them, might be useful for researchers and policy makers, because the subfields and their interrelations indicate the whole pattern of the field at a glance
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