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July 24, 2026
'Physics Is What the World Is Literally Built On'
Physicist Nina Dzhanayeva, recipient of a Vladimir Potanin Foundation scholarship, focuses her research on nanophotonics. In this interview for the HSE Young Scientists project, she discusses nanowells, scientific intuition, and how physics can help in making frangipane cream puffs.
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.

 

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Взаимосвязь сетевых характеристик и субъектности сетевых сообществ в социальной сети Твиттер

Вопросы кибербезопасности. 2020. Т. 37. № 3. С. 40–57.
Chepovskiy A., Воронин А. Н., Ковалева Ю. В.

The purpose of the study: analysis of the graph of interacting objects of social networks based on the selection of implicit communities, assessment of the subjectivity of the selected communities and comparison of the network characteristics of communities and various indicators of their subjectivity.

Method: communities detection on the constructed weighted graph of a social network, psycholinguistic analysis of community content using a list of discourse markers of subjectivity, statistical methods for identifying the relationship between network characteristics and the frequency of discourse markers.

Results: algorithms to construct a graph and to import user attributes were developed, an algorithm for dividing a weighted graph into implicit user communities was implemented, the subjectivity of the content of the selected network communities in the social network Twitter has was assessed, the relationship and directional shift in the connectivity of the graph and various indicators of the subjectivity of the network community were identified.

Research target: Computer Science Psychology
Priority areas: IT and mathematics
Language: Russian
Full text
DOI
Keywords: social subjectivitysocial network analysisnetwork communityвыделение сообществанализ социальной сетиdiscourse markersдискурсивные маркерысоциальный графgraph community detection
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