A principled methodology for comparing relatedness measures for clustering publications

TL;DR

This paper introduces a formal, consistent methodology for evaluating and comparing different relatedness measures used in clustering scientific publications, validated through empirical analysis across multiple fields.

Contribution

It proposes a principled, consistency-preserving evaluation framework for relatedness measures and demonstrates its effectiveness through empirical studies in various scientific domains.

Findings

Bibliographic coupling yields more accurate clusters than citation and co-citation.

Extended direct citation performs comparably or slightly better than bibliographic coupling.

Text-based BM25 measure outperforms other measures when used as an evaluation criterion.

Abstract

There are many different relatedness measures, based for instance on citation relations or textual similarity, that can be used to cluster scientific publications. We propose a principled methodology for evaluating the accuracy of clustering solutions obtained using these relatedness measures. We formally show that the proposed methodology has an important consistency property. The empirical analyses that we present are based on publications in the fields of cell biology, condensed matter physics, and economics. Using the BM25 text-based relatedness measure as evaluation criterion, we find that bibliographic coupling relations yield more accurate clustering solutions than direct citation relations and co-citation relations. The so-called extended direct citation approach performs similarly to or slightly better than bibliographic coupling in terms of the accuracy of the resulting clustering solutions. The other way around, using a citation-based relatedness measure as evaluation criterion, BM25 turns out to yield more accurate clustering solutions than other text-based relatedness measures.