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July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.
July 20, 2026
Scientists Propose Method for More Efficient Resource Use in Machine Learning
An international group of researchers, including mathematicians from the AI and Digital Science Institute at the HSE Faculty of Computer Science, has provided a theoretical justification for a simple and computationally efficient method of estimating uncertainty in Stochastic Gradient Descent (SGD). The paper has been published on the scientific preprint server arXiv.org and presented at AISTATS 2026.

 

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Qualitative social network analysis: studying the field through the bibliographic approach

Quality and Quantity. 2024. No. 58. P. 385–411.
Kim A., Maltseva D.

This paper presents the results of a study on the development of qualitative social network anal- ysis (QSNA) and its evolution over time, using the analysis of bibliographic networks. The dataset consists of articles from the Web of Science Clarivate Analytics database obtained by searching for the keyword ”Social network* + (Qualitative OR Mixed method*)” (in total 21,823 publications). From the data, we constructed a citation network, which was followed by an evaluation of QSNA field’s growth and the identification of the most cited works. Using the normalized Search path count weights, we extracted the Main path and Key-routes in the citation network. Starting from 1988, when the first works appeared, the field has grown significantly. The analysis of the citation network shows that there are two main scientific spheres where QSNA was developed - in the social science and the medical science. In the social science the main empirical research subjects are migrant net- works and teacher’s networks. In the medical science the research object moved from sexual contacts to social contacts in infection studies.

Research target: Social Sciences
Language: English
DOI
Text on another site
Keywords: citation networkqualitative social network analysissearch path count web of scienceDevelopment of scientific fields
Publication based on the results of:
Development of network analysis in Russia: adaptation of theoretical and methodological approaches and practical application (2024)
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