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Scientists forecast scientific concept diffusion using AI models

Researchers have developed a new method to forecast the diffusion of scientific concepts, focusing on quantum computing as a case study. By analyzing concept co-occurrence networks and citation patterns, they trained models to predict how concepts spread within and across scientific fields. The study found that while endogenous reinforcement of concepts is hard to predict, exogenous diffusion and entropy are highly predictable, driven by factors like upstream heterogeneity and citation breadth. AI

IMPACT Provides a scalable foundation for anticipatory scientometrics and technology foresight in evolving research fields.

RANK_REASON The cluster contains an academic paper detailing a new research methodology and findings.

Read on arXiv cs.LG →

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Thomas Maillart, Thibaut Chataing, David Dosu, Paul Bagourd, Julian Jang-Jaccard, Alain Mermoud ·

    Forecasting Conceptual Diffusion in Science: The Case of Quantum Computing

    arXiv:2606.03919v1 Announce Type: cross 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 con…

  2. arXiv cs.LG TIER_1 English(EN) · Alain Mermoud ·

    Forecasting Conceptual Diffusion in Science: The Case of Quantum Computing

    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…