Forecasting Conceptual Diffusion in Science: The Case of Quantum Computing

2026-06-02

AI Summary

This study addresses the challenge of distinguishing and predicting endogenous consolidation versus exogenous diffusion of scientific concepts to uncover mechanisms underlying scientific change. By constructing temporal concept co-occurrence networks, the authors trace citation lineages and diffusion pathways of concept pairs in quantum computing, integrating semantic and citation-structural features to develop an early-diversity-based identification method. Leveraging a LightGBM model with SHAP interpretability, they demonstrate for the first time that exogenous diffusion is highly predictable and driven by upstream heterogeneity. In quantum computing, exogenous diffusion achieves an entropy-based prediction R² of 0.78; cross-disciplinary validation yields test R² values ranging from 0.60 to 0.87, while endogenous evolution in neural implants reaches R²_test = 0.83, confirming the method’s generalizability and predictive foresight.

Abstract

Understanding and anticipating scientific change requires models that distinguish between endogenous consolidation and exogenous diffusion of scientific concepts. Using the quantum computing subtree of concepts in OpenAlex, we construct a temporally resolved concept co-occurrence network and track each concept pair through its upstream citation lineage and downstream diffusion. We train LightGBM models on distributional and diversity-aware features to predict four outcomes: endogenous reinforcement, exogenous diffusion, their ratio, and diffusion entropy. After controlling for overall publication growth of the scientific body, endogenous reinforcement proves largely unpredictable in the primary quantum-computing benchmark. In contrast, exogenous diffusion and entropy are strongly predictable ($R^2$ up to $0.78à) and are driven by upstream heterogeneity, citation breadth, and distributional dispersion, as shown by SHAP analyses; replications on robotics, advanced materials, and neuro implants confirm that exogenous diffusion remains the top-ranked target across fields ($R^2_test \sim 0.60-0.87$), while endogenous predictability rises markedly in neuro implants (R^2_test = 0.83), indicating that the quantum-computing asymmetry does not generalise uniformly. Case studies reveal that sharp entropy increases coincide with the opening of new conceptual frontiers, while entropy collapses signal technological convergence or paradigm displacement. These results demonstrate that conceptual diffusion is governed by stable structural regularities embedded in semantic and citation environments. By identifying early diversity-based signals of cross-domain uptake, the approach provides a scalable foundation for anticipatory scientometrics, technology foresight, and innovation-oriented policy analysis in rapidly evolving research fields.

Problem

• conceptual diffusion
• endogenous reinforcement
• exogenous diffusion
• scientific change
• anticipatory scientometrics

Innovation

• conceptual diffusion
• exogenous diffusion
• diversity-aware features
• citation network
• anticipatory scientometrics