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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Encyclopedia of Social Network Analysis and Mining. 2nd Ed.

NY : Springer, 2017.
Editor-in-chief: R. Alhajj, J. Rokne

  

Chapters
Pajek and PajekXXL
Batagelj V., Mrvar A., , in: Encyclopedia of Social Network Analysis and Mining. 2nd Ed.: NY: Springer, 2017. P. 1–13.
Tool’s ID Card Tool name, title: Pajek and PajekXXL, program for analysis and visualization of large networks Creation year: November 1996 Authors: Vladimir Batagelj and Andrej Mrvar Range: general network problems with emphasis on large networks Copyright: free for noncommercial use ...
Added: November 2, 2018
Python packages for networks
Batagelj V., , in: Encyclopedia of Social Network Analysis and Mining. 2nd Ed.: NY: Springer, 2017..
Python is a high-level general-purpose programming language and easy to understand and learn. To deal with networks, different representations of networks in Python were proposed. On their basis, several Python libraries were developed to support programming of network analysis tasks. ...
Added: November 2, 2018
Semirings and matrix analysis of networks
CERINŠEK M., Batagelj V., , in: Encyclopedia of Social Network Analysis and Mining. 2nd Ed.: NY: Springer, 2017..
A network can be represented also with a corresponding matrix. Using matrix operations (addition and multiplication) over an appropriate semiring a unified approach to several network analysis problems can be developed. Matrix multiplication is about traveling on network. ...
Added: November 2, 2018
Sources of network data
CERINŠEK M., Batagelj V., , in: Encyclopedia of Social Network Analysis and Mining. 2nd Ed.: NY: Springer, 2017. P. 1–9.
In network data different entities are linked through their relations. They can be found in many forms and obtained from observations, surveys, archives, databases, etc. ...
Added: November 2, 2018
Priority areas: sociology IT and mathematics
Language: English
DOI
Text on another site
Keywords: network analysissocial network analysisencyclopedia
Encyclopedia of Social Network Analysis and Mining. 2nd Ed.
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The book contains new models of bibliometric analysis based on centrality measures in network analysis, pattern analysis and stability analysis. A distinctive feature of these centrality measures is that they account for the parameters of vertices and group influence of vertices to a vertex. This reveals specific groups of publications, authors, terms, journals and affiliations ...
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