SilhouetteScoreinR: Beyond traditional network layouts by leveraging local cohesion and nearest neighbor separation

The silhouette score (SS) quantifies how well each entity fits its assigned cluster by contrasting within-cluster cohesion with nearest other-cluster separation. Although common in other fields, SS is rarely used in bibliometrics. Using 2,252 <i>MethodsX</i> articles (2020-2024), we show how SS evaluates clustering quality in co-word networks and author collaborations, independent of the chosen algorithm. We provide R scripts to compute SS for explicit (geographic/known coordinates) and implicit (PCA/UMAP) layouts and introduce a two-axis visualization that plots publication count against SS. The framework highlights coherent clusters (high SS) and flags boundary or misassigned entities (low/negative SS) that standard network plots can obscure. This improves interpretability at term, cluster, and corpus levels and supports more defensible decisions about labels, membership, and follow-up analysis. Code is released for replication and reuse; sensitivity to distance metrics and data regimes is discussed to guide application across bibliometrics and related domains.•Silhouette Scores Reveal Outliers: Silhouette scores not only validate cluster cohesion but also uncover meaningful outliers-insights often missed in traditional network layouts.•Novel Visualization Approach: Combining silhouette scores with publication counts enables a more nuanced visualization of co-word and collaboration networks.•Applied to Bibliometrics: This study applies silhouette analysis to 2252 MethodsX articles, offering new tools for evaluating clustering quality in bibliometric research.