Abstract
We performed a scientometric analysis of Chaos papers from 1991 to 2019, applying a careful disambiguation process for identifying the authors correctly. First, we used standard scientometric tools based on descriptive statistics. This analysis enabled us to compute productivity and the degree of collaboration. The evolution in the number of authors, countries, and topics per article has an increasing trend. An analysis of the citations considering their temporal mean number exhibits a growing tendency in time. Second, we dealt with Lotka–Zipf’s law considering the rank distributions of 15 datasets. We found that the sum of Crossref citations by country was the only dataset for which the power-law was the only plausible distribution. Next, we examined the networks of authors, countries, and topics, going from the simplest case of undirected and unweighted networks to the general case of weighted and directed networks and assigning a weight to the individual nodes. Based on the networks’ topology and features, we introduced diversity, collaboration, influence, and productivity measures and found a significant increase in the diversity of all the considered networks (authors, countries, and topics) but manifesting a very different network structure. The computation of typical network quantities combined with the communities’ identification reveals the presence of several hubs and the existence of various communities that encompass nodes of all the continents in the case of countries. Finally, using the most general networks, it was possible to compute influence and productivity indexes to find the USA, China, and Germany’s leadership inside the network.
In the last few years, scientometrics turned into one essential tool to evaluate scientific production’s impact and one approach to elaborate policies for improving scientific advances and distribute the funds efficiently to research institutions and projects that could potentially impact. Scientific publications are crucial to endorse work efficiency, the quality of research, and the collaborations among scientists, institutions, and countries. During the last two decades, complex network analyses emerged as a fundamentally important tool since almost all systems can be represented by complex networks regardless of their nature. In this work, we combine scientometric measures with a complex network analysis from different standpoints, including the most general descriptions such as the concept of time-variable weighted networks and the nodes’ relevance even if they might be isolated. The features mentioned above could be the basis for further advances in scientometrics and complex networks theory. The network analysis’s main results indicate that diversity and collaboration have increased markedly and lead in influence and productivity of the USA, China, and Germany within Chaos
