Bibliometric studies based on the Web of Science (WOS) database have become an increasingly popular method for analysing the structure of scientific research. So do network approaches, which, based on empirical data, make it possible to characterize the emergence of topological structures over time and across multiple research areas. Our paper is a contribution to interweaving these two lines of research that have progressed in separate ways but whose common applications have been increasingly more frequent. Among other attributes, Author Keywords and Keywords Plus are used as units of analysis that enable us to identify changes in the topics of interest and related bibliography. By considering the co-occurrence of those keywords with the Author Keyword \texttt{Complexity}, we provide an overview of the evolution of studies on Complexity Sciences, and compare this evolution in seven scientific fields. The results show a considerable increase in the number of papers dealing with complexity, as well as a general tendency across different disciplines for this literature to move from a more foundational, general and conceptual to a more applied and specific set of co-occurring keywords. Moreover, we provide evidence of changing topologies of networks of co-occurring keywords, which are described through the computation of some topological coefficients. In so doing, we emphasize the distinguishing structures that characterize the networks of the seven research areas.

翻译：基于Web of Science (WOS)数据库的文献计量研究已成为分析科学研究结构的日益流行的方法。同样，基于经验数据的网络方法使得刻画跨时间、跨多个研究领域的拓扑结构涌现成为可能。本文旨在弥合这两条独立发展但共同应用日益频繁的研究路径。在众多属性中，作者关键词和关键词增强被用作分析单元，使我们能够识别研究兴趣及相关文献的变化。通过考察这些关键词与作者关键词“复杂性”的共现关系，我们概述了复杂性科学研究的演化历程，并比较了其在七个科学领域中的演化差异。结果表明，涉及复杂性的论文数量显著增加，且不同学科普遍呈现出从更基础、通用和概念性的共现关键词集合向更应用和具体化的方向转变。此外，我们通过计算若干拓扑系数，描述了共现关键词网络拓扑结构的变化，并由此强调了表征七个研究领域网络的区分性结构。