不同规模和特性的科学界的网络集合
Network assembly of scientific communities of varying size and specificity
摘要：
研究人员的合作网络如何围绕一个科学主题联合起来？当一个新的领域发展时，会发生什么样的社会结构调整？以前对这些问题的实证探索已经研究了与几个科学领域相关的合著网络的演变，每个都注意到网络结构随着领域发展而发生的特征性变化。然而，从历史上看，这类研究往往依赖于手动注释的数据集，因此只考虑少数学科，从而质疑观察到的结构特征的普遍性。为了克服这一限制并测试这一现象的鲁棒性，我们使用了超过189，000篇科学文章的综合数据集，并开发了一个框架，用于将文章及其作者划分为连贯的，语义相关的组，代表不同规模和特异性的科学领域。然后，我们使用由此产生的人口的领域来研究不断发展的合著网络的结构。与早期的研究结果一致，我们观察到一个全球性的拓扑结构转变的合著网络合并从一个脱节的聚合成一个密集的巨大的连接组件，占主导地位的网络。我们使用一个单独的，免费的科学文章语料库来验证这些结果，总体而言，我们发现以前报道的科学领域的相关合著网络的特征结构演变在大量不同规模，范围和特异性的科学领域中是稳健的。此外，在这项研究中开发的框架可以用于其他scientometric背景下，以扩展研究，在更大范围的科学学科进行比较。
英文摘要：
How does the collaboration network of researchers coalesce around a scientific topic? What sort of social restructuring occurs as a new field develops? Previous empirical explorations of these questions have examined the evolution of co-authorship networks associated with several fields of science, each noting a characteristic shift in network structure as fields develop. Historically, however, such studies have tended to rely on manually annotated datasets and therefore only consider a handful of disciplines, calling into question the universality of the observed structural signature.To overcome this limitation and test the robustness of this phenomenon, we use a comprehensive dataset of over 189,000 scientific articles and develop a framework for partitioning articles and their authors into coherent, semantically-related groups representing scientific fields of varying size and specificity. We then use the resulting population of fields to study the structure of evolving co-authorship networks. Consistent with earlier findings, we observe a global topological transition as the co-authorship networks coalesce from a disjointed aggregate into a dense giant connected component that dominates the network. We validate these results using a separate, complimentary corpus of scientific articles, and, overall, we find that the previously reported characteristic structural evolution of a scientific field's associated co-authorship network is robust across a large number of scientific fields of varying size, scope, and specificity. Additionally, the framework developed in this study may be used in other scientometric contexts in order to extend studies to compare across a larger range of scientific disciplines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
