Title: Predicting Conceptual Spread in Science: Insights from Quantum Computing Research

To effectively understand and forecast shifts in scientific paradigms, it is essential to employ models that can differentiate between the internal consolidation of ideas (endogenous reinforcement) and their external spread across disciplines (exogenous diffusion). This study leverages the quantum computing domain within the OpenAlex database to build a time-sensitive network of concept co-occurrences. By tracing the citation history upstream and monitoring diffusion patterns downstream for each concept pair, the research aims to predict specific scientific outcomes.

The authors utilized LightGBM models trained on features that account for both distributional characteristics and diversity metrics. These models were designed to forecast four key variables: the extent of endogenous reinforcement, the degree of exogenous diffusion, the ratio between the two, and the associated diffusion entropy. When controlling for the general growth in publications within the scientific community, the primary benchmark focused on quantum computing revealed that endogenous reinforcement is largely unpredictable. However, exogenous diffusion and entropy levels proved to be highly predictable, achieving $R^2$ values as high as 0.78.

SHAP (SHapley Additive exPlanations) analyses identified that this predictability is driven by factors such as upstream heterogeneity, the breadth of citations, and distributional dispersion. To test the robustness of these findings, the researchers replicated the study in other fields, including robotics, advanced materials, and neuroimplants. These replications confirmed that exogenous diffusion remains the most consistently predictable target across various disciplines, with test-set $R^2$ scores ranging from 0.60 to 0.87. Notably, endogenous predictability increased significantly in the neuroimplants field ($R^2_test = 0.83$), suggesting that the unique asymmetry observed in quantum computing does not apply uniformly across all scientific areas.

Case studies further illustrated that sudden spikes in entropy often correspond to the emergence of new conceptual frontiers, whereas declines in entropy indicate technological convergence or a shift in paradigms. Ultimately, these findings highlight that conceptual diffusion is regulated by consistent structural patterns inherent in both semantic and citation landscapes. By detecting early signals of diversity that precede cross-domain adoption, this methodology offers a scalable framework for anticipatory scientometrics, technology forecasting, and policy analysis aimed at fostering innovation in fast-moving research sectors.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC