研究领域形成过程中知识基础的模块性下降
Declining Modularity of Intellectual Bases During the Emergence of Research Areas
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中文总结 AI 辅助
该研究提出框架追踪共引网络模块性变化，发现研究领域形成时模块性下降，将其应用于三个领域验证，表明该下降可作为整合式形成的结构特征。
中文摘要 AI 辅助
理解研究领域如何形成有助于及早识别新兴领域并为研究策略提供依据，但随着领域成型，其知识基础如何重组仍不明确。我们假设研究领域的形成伴随着本质上相互独立的知识共同体的整合，这可通过作为其知识基础的共引网络模块性的下降来观测。我们提出一个框架，用于随时间追踪该模块性、评估其变化的统计稳健性，并识别与模块性下降高度相关的论文。我们将该框架应用于三种具有不同增长模式的领域：高阶网络科学、超弦理论和图表示学习。在这三个领域中，模块性均随各领域的形成或转变而下降，且超弦理论的模块性下降与独立记录的转变过程相符。对高阶网络科学的进一步分析显示，其模块性下降反映了跨学科整合；而图表示学习中，模块性的逐步下降后出现上升，我们将其解读为形成期后的再分化。我们的结果表明，共引网络模块性的下降可作为结构特征，回溯性地表征这种整合式的形成模式。
英文摘要
Understanding how research areas emerge can help identify nascent areas early and inform research strategy, yet how the intellectual base of a field restructures as an area takes shape remains unclear. We hypothesize that the emergence of a research area is accompanied by the integration of largely separate knowledge communities, observable as a decline in the modularity of its co-citation network, which represents its intellectual base. We propose a framework that tracks this modularity over time, evaluates the statistical robustness of its changes, and identifies the papers highly associated with the decline. We applied it to three areas with different modes of growth: higher-order network science, superstring theory, and graph representation learning. In all three, modularity declined in correspondence with each area's emergence or transformation, and in superstring theory, the decline aligns with an independently documented transition. Further analysis of higher-order network science shows that its decline reflects a cross-disciplinary integration. In graph representation learning, the gradual decline is followed by a rise, which we interpret as a re-differentiation after the emergence period. Our results suggest that a decline in the modularity of a co-citation network can serve as a structural signature that retrospectively characterizes this integrative mode of emergence.
