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News
July 9, 2026
HSE Economists Use Search Queries to Forecast Birth Rates
Researchers from the HSE Faculty of Economic Sciences have shown that the accuracy of birth rate forecasts for Russia can be improved by almost 50% by incorporating the dynamics of online search queries related to pregnancy and childbirth into forecasting models. In the best-performing models, the forecasting error fell from 4.6% to 3.2%. The findings have been published in Populations and Economics.
July 8, 2026
HSE Researchers Discover Who Eats Out in Russia-And Why
Around one-third of Russians (31.3%) rarely eat out or buy ready-made meals. The core group of active consumers—those who eat out or purchase prepared food almost every day or several times a week—accounts for only about 9% of the population. These are the findings of a study conducted by the HSE Institute for Social Policy. According to the researchers eating out is no longer a marker of high social status in Russia.
July 8, 2026
HSE University and RREDA Join Forces to Support 2026 Renewable Energy of the Planet Competition
HSE University and the Russia Renewable Energy Development Association (RREDA) have signed a partnership and information cooperation agreement to support Renewable Energy of the Planet—2026, a national competition with international participation for students and early-career researchers. Applications are open on the competition's website until September 20, 2026.

 

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2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Association for Computing Machinery (ACM), 2020.

ASONAM '20: International Conference on Advances in Social Networks Analysis and Mining, 7-10 December 2020, The Hague, Netherlands (Virtual).

Chapters
A Data Recovery Method for Community Detection in Feature-Rich Networks
Mirkin B., , in: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).: Association for Computing Machinery (ACM), 2020. Ch. 05 P. 99–105.
The problem of community detection in a network with features at its nodes takes into account both the graph structure and node features. The goal is to find relatively dense groups of interconnected entities sharing some features in common. We apply the so-called data recovery approach to the problem by combining the least-squares recovery criteria ...
Added: October 31, 2020
A Data Recovery Method for Community Detection in Feature-Rich Networks
Shalileh S., Mirkin B., , in: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).: Association for Computing Machinery (ACM), 2020. P. 99–104.
The problem of community detection in a network with features at its nodes takes into account both the graph structure and node features. The goal is to find relatively dense groups of interconnected entities sharing some features in common. We apply the so-called data recovery approach to the problem by combining the least-squares recovery criteria ...
Added: December 11, 2020
Priority areas: economics IT and mathematics mathematics engineering science
Language: English
DOI
Keywords: machine learningclustering and classificationcommunity detection algorithmssocial networks analysiscommunity detectionartificial intellegence
2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
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Hybrid Competition under Fragmented Demand and Limited Consumer Choice
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