Year: 2025
Type: article
Abstract: Scientific journals play an important role as a key tool for advancing science and encouraging knowledge exchange during research collaboration. The relationships among them are extremely diverse and cluster together into a complex network of connections. Developing adequate mathematical models of such self-organizing systems is a serious problem that requires in-depth investigation. This article introduces an approach to analyze the journal network based on a new bibliographic graph in which the relationships among journals are defined by shared authorship, i.e., through the binary intersection of author sets. Two levels of journal interaction can be distinguished using consistent source data processing techniques and methods. At the lower level, the relationships among multiple journals are grouped by scientific topics. At the upper level, the interactions between these topics are established. The approach was validated through a pilot study of eLIBRARY.RU data. The findings demonstrate its feasibility and scalability potential.
Author: A. A. Pechnikov
Institutions: Russian Academy of Sciences, Karelian Research Centre
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4412526104