Density, asymmetry and citation dynamics in scientific literature
This work addresses how scientific novelty versus building on existing knowledge influences impact, offering a scalable framework for scientometrics, but it is incremental as it refines existing methods with new metrics.
The study investigated whether a paper's similarity to previous research, measured by density and asymmetry in semantic space, predicts its citation rate, finding that density modestly improves predictions across disciplines while asymmetry does not.
Scientific behavior is often characterized by a tension between building upon established knowledge and introducing novel ideas. Here, we investigate whether this tension is reflected in the relationship between the similarity of a scientific paper to previous research and its eventual citation rate. To operationalize similarity to previous research, we introduce two complementary metrics to characterize the local geometry of a publication's semantic neighborhood: (1) \emph{density} ($ρ$), defined as the ratio between a fixed number of previously-published papers and the minimum distance enclosing those papers in a semantic embedding space, and (2) asymmetry ($α$), defined as the average directional difference between a paper and its nearest neighbors. We tested the predictive relationship between these two metrics and its subsequent citation rate using a Bayesian hierarchical regression approach, surveying $\sim 53,000$ publications across nine academic disciplines and five different document embeddings. While the individual effects of $ρ$ on citation count are small and variable, incorporating density-based predictors consistently improves out-of-sample prediction when added to baseline models. These results suggest that the density of a paper's surrounding scientific literature may carry modest but informative signals about its eventual impact. Meanwhile, we find no evidence that publication asymmetry improves model predictions of citation rates. Our work provides a scalable framework for linking document embeddings to scientometric outcomes and highlights new questions regarding the role that semantic similarity plays in shaping the dynamics of scientific reward.