用引用网络预测科学概念扩散，发现外源扩散比内生强化更可预测。
Forecasting Conceptual Diffusion in Science: The Case of Quantum Computing
- 构建概念共现网络，用分布与多样性特征预测扩散行为。
- 外源扩散和熵值预测准确率最高达0.78，由引用多样性驱动。
- 适用于技术预见与政策分析，尤其适合快速演化的研究领域。
理解与预测科学变革需区分概念的内生整合与外源扩散。基于OpenAlex中的量子计算概念子树，构建时间解析的概念共现网络，追踪每对概念的上游引用谱系与下游扩散路径。使用LightGBM模型，基于分布与多样性感知特征预测四项结果：内生强化、外源扩散、二者比例及扩散熵。在控制整体发文增长后，主基准中内生强化难以预测；而外源扩散与熵值预测性能优异（$R^2$最高达0.78），其由上游异质性、引文广度与分布离散性驱动，经SHAP分析验证。在机器人学、先进材料与神经植入物领域的复现表明，外源扩散始终为最佳预测目标（$R^2_test \sim 0.60-0.87$），而神经植入物中内生预测能力显著提升（$R^2_test = 0.83$），说明量子计算中的不对称性不具普遍性。案例研究显示，熵值突增对应新概念前沿开启，熵值坍缩则预示技术收敛或范式更替。结果表明，概念扩散受语义与引用环境中的稳定结构规律支配。通过识别跨领域采纳的早期多样性信号，该方法为前瞻性科学计量学、技术预见与创新导向政策分析提供可扩展基础。
原文摘要 · Abstract (English)
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.
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