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September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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New Centrality Measures in Networks: How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

CRC Press, 2022.
Aleskerov F. T., Швыдун С. В., Meshcheryakova N.

Over the last number of years there has been a growing interest in the analysis of complex networks which describe a wide range of real-world systems in nature and society. Identification of the central elements in such networks is one of the key research areas. Solutions to this problem are important for making strategic decisions and studying the behavior of dynamic processes, e.g. epidemic spread. The importance of nodes has been studied using various centrality measures. Generally, it should be considered that most real systems are not homogeneous: nodes may have individual attributes and influence each other in groups while connections between nodes may describe different types of relations. Thus, critical nodes detection is not a straightforward process.

New Centrality Measures in Networks presents a class of new centrality measures which take into account individual attributes of nodes, the possibility of group influence and long-range interactions and discusses all their new features. The book provides a wide range of applications of network analysis in several fields – financial networks, international migration, global trade, global food network, arms transfers, networks of terrorist groups, and networks of international journals in economics. Real-world studies of networks indicate that the proposed centrality measures can identify important nodes in different applications. Starting from the basic ideas, the development of the indices and their advantages compared to existing centrality measures are presented.

Features:

  • Built around real-world case studies in a variety of different areas (finance, migration, trade, etc.)
  • Suitable for students and professional researchers with an interest in complex network analysis
  • Paired with a software package for readers who wish to apply the proposed models of centrality (in Python) available at https://github.com/SergSHV/slric
Research target: Computer Science
Language: English
Sample Chapter
DOI
Text on another site
Keywords: анализ социальных сетейвлияниеnetwork analysisinterdependencecentralityиндексы центральностиindirect influenceвлияние в сетяхInfluence in networksShort-range interaction centralityLong-range interaction centrality
New Centrality Measures in Networks: How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes
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