Network-based Scientometric Analysis

Co-citation analysis links two documents when a third document cites both — it captures perceived intellectual similarity as judged by subsequent authors and reflects the intellectual heritage of a field. Bibliographic coupling links two documents when they share at least one reference — it captures similarity in source material and is more useful for identifying current research fronts. Both are valid network types for different research questions about scholarly structure.

There is no strict minimum, but networks with fewer than about 50 nodes tend to be too sparse for community detection to produce stable, interpretable clusters. Analyses based on hundreds to tens of thousands of records are most common and most robust. Very large networks (above ~50,000 nodes) may require computational optimization or filtering by minimum link strength.

VOSviewer is the most widely used tool for bibliometric network visualization and is particularly user-friendly for co-citation, bibliographic coupling, and co-word networks. Gephi offers more flexible visualization and a wider range of community-detection algorithms but requires more technical skill. The R package bibliometrix provides the most comprehensive analytical suite (including longitudinal analysis and factorial methods) and is preferred when reproducibility via scripting is a priority. Many studies use two or more tools in combination.

No. Scientometric network analysis describes the structural properties of a literature — who cites whom, which topics cluster together — but it does not assess the quality or content of individual studies. It is best positioned as a preliminary mapping step that informs scope, identifies key papers for full-text review, and provides a visual overview, rather than as a substitute for content synthesis or quality appraisal.

The resolution parameter in algorithms such as Louvain controls the granularity of clusters: lower values merge communities into fewer, broader clusters; higher values split them into more, narrower ones. A common practice is to run the algorithm at multiple resolution settings, inspect the resulting clusters, and select the setting that produces thematically coherent and interpretable groupings — guided by domain knowledge rather than a single statistical criterion.