Determining the Thematic Proximity of Scientific Journals and Conferences Using Big Data Technologies

The number of scientific journals published in the world is very large. In this regard, it is necessary to create software tools that will allow analyzing thematic links of journals. The algorithm presented in this paper uses graphs of co-authorship for analyzing the thematic proximity of journals. It is insensitive to the language of the journal and can find similar journals in different languages. This task is difficult for algorithms based on the analysis of full-text information. Approbation of the algorithm was carried out in the scientometric system IAS ISTINA. Using a special interface, a user can select one interesting journal. Then the system will automatically generate a selection of journals that may be of interest to the user. In the future, the developed algorithm can be adapted to search for similar conferences, collections of publications and research projects. The use of such tools will increase the publication activity of young employees, increase the citation of articles and quoting between journals. In addition, the results of the algorithm for determining thematic proximity between journals, collections, conferences and research projects can be used to build rules in the ontology models for access control systems.