With the advent of network science, it has become possible to explore complex network systems, including social and information networks, by presenting them as graph models. The exponential growth of the total volume of scientific publications determines the relevance of the tasks of analyzing their interrelations. In network science, models and methods related to the field of so-called citation networks are being developed to solve these problems. However, network metrics are not used when analyzing publications in citation databases. The paper considers the issues of creating a decision support system for the selection of information sources based on data on the citation of scientific publications. A software package has been developed for making decisions on determining an important publication in a certain thematic area. The software package is based on a method of ranking publications by importance based on the analysis of citation networks, which allows you to identify publications that do not clearly stand out when ranking based on known bibliometric indicators or known measures of centrality of nodes in their pure form. A study and comparative analysis of software for visualization and research of all types of graphs and social networks has been conducted. Studies have been carried out confirming the effectiveness of the proposed decision support system in the selection of information sources.

Starting from spring 2022 a national system for evaluating the effectiveness of scientific research and development is being created using the database of leading Russian journals Russian Science Citation Index (RSCI). The article represents some results of the analysis of the RSCI normalized rating published in December 2022. Using the journal citation graph constructed from the data Math-Net.Ru on the example of thematic group OECD 101.Mathematics we show that in the case of a large number of self-citations of the journals, a relationship between the normalized rating and the number of references is found, and the relationship with self-citation is somewhat stronger than without self-citation. Analysis of the distribution of the journals by thematic groups shows that using a single attribute, such as the OECD, as a criterion does not allow forming a group as a community of journals with a single strongly connected component in the citation graph. We conclude that the research methods of journal citation graphs are a good basis for a comparative analysis of the characteristics of journals and their ranking, which means they can be used as tools for further development and improvement of journal ratings.