Scientometric analysis of the Chaos journal (1991-2019): From descriptive statistics to complex networks viewpoints

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.