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School of Data Analysis and Artificial Intelligence
Faculty of Computer Science
School of Data Analysis and Artificial Intelligence
Contacts

Pokrovsky boulevard, 11, room S938, Moscow, Russia, 109028

Phone: +7 (495) 772-95-90*27319

Administrations
About the School

The School of Data Analysis and Artificial Intelligence was created in 2014 as part of the Department of Data Analysis and Artificial Intelligence. The school consists of world-renowned researchers who actively participate in international research projects.

Publications
Article
Efficient indexing of peptides for database search using Tide

Acquaye F. L., Kertesz-Farkas A., Stafford Noble W.

Journal of Proteome Research. 2023. Vol. 22. No. 2. P. 577-584.

Article
Generalized relational tensors for chaotic time series

Vasilii A. Gromov, Yury N. Beschastnov, Korney K. Tomashchuk.

PeerJ Computer Science. 2023. Vol. 9. No.  .

Article
Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets

Makhalova T., Kuznetsov S., Napoli A.

Data Mining and Knowledge Discovery. 2022. P. 108-145.

Article
Human knowledge models: Learning applied knowledge from the data

Dudyrev E., Semenkov Ilia, Kuznetsov S. et al.

Plos One. 2022. Vol. 17. No. 10.

Book chapter
Modeling Generalization in Domain Taxonomies Using a Maximum Likelihood Criterion

Zhirayr Hayrapetyan, Nascimento S., Trevor F. et al.

In bk.: Information Systems and Technologies: WorldCIST 2022, Volume 2. Iss. 469. Springer, 2022. P. 141-147.

Book chapter
Ontology-Controlled Automated Cumulative Scaffolding for Personalized Adaptive Learning

Dudyrev F., Neznanov A., Anisimova K.

In bk.: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium -23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part II. Springer, 2022. P. 436-439.

Book chapter
Triclustering in Big Data Setting

Egurnov D., Точилкин Д. С., Ignatov D. I.

In bk.: Complex Data Analytics with Formal Concept Analysis. Springer, 2022. P. 239-258.

Article
Triclusters of Close Values for the Analysis of 3D Data

Egurnov D., Ignatov D. I.

Automation and Remote Control. 2022. Vol. 83. No. 6. P. 894-902.

Article
Deep Convolutional Neural Networks Help Scoring Tandem Mass Spectrometry Data in Database-Searching Approaches

Kudriavtseva P., Kashkinov M., Kertész-Farkas A.

Journal of Proteome Research. 2021. Vol. 20. No. 10. P. 4708-4717.

Article
Language models for some extensions of the Lambek calculus

Kanovich M., Kuznetsov S., Scedrov A.

Information and Computation. 2022. Vol. 287.

All publications

Network Science

2024/2025
Academic Year
ENG
Instruction in English
6
ECTS credits
Delivered by:
School of Data Analysis and Artificial Intelligence
Type:
Elective course
When:
1 year, 3, 4 module

Instructors

Kogan, Alexandra

Kogan, Alexandra

Pozdnyakov, Vitaliy

Pozdnyakov, Vitaliy

Course Syllabus

Full SyllabusAsk Question

Abstract

The course “Network Science” introduces students to new and actively evolving interdisciplinary field of network science. Started as a study of social networks by sociologists, it attracted attention of physicists, computer scientists, economists, computational biologists, linguists and others and become a truly interdisciplinary field of study. In spite of the variety of processes that form networks, and objects and relationships that serves as nodes and edges in these networks, all networks poses common statistical and structural properties. The interplay between order and disorder creates complex network structures that are the focus of the study. In the course we will consider methods of statistical and structural analysis of the networks, models of network formation and evolution and processes developing on network. Special attention will be given to the hands-on practical analysis and visualization of the real world networks using available software tools and modern programming languages and libraries.
Learning Objectives

Learning Objectives

  • To familiarize students with a new rapidly evolving filed of network science, and provide practical knowledge experience in analysis of real world network data.
Expected Learning Outcomes

Expected Learning Outcomes

  • Know basic notions and terminology used in network science
  • Understand fundamental principles of network structure and evolution.
  • Can develop mathematical models of network processes.
  • Can analyze real world network data.
Course Contents

Course Contents

  • Introdiction to network science
  • Scale-free networks
  • Random networks
  • Network models
  • Node centrality and ranking on networks
  • Structural properties of networks
  • Community detection in networks
  • Epidemics on networks
  • Cascades and influence maximization
  • Node classification
  • Link prediction
  • Graph embedding
  • Graph neural networks
  • GNNs in practice
  • Theoretical foundations of GNNs
  • Knowledge graphs
Assessment Elements

Assessment Elements

  • non-blocking Домашнее задание
  • non-blocking Тест
  • non-blocking Индивидуальный проект
  • non-blocking Соревнование
  • non-blocking Экзамен
Interim Assessment

Interim Assessment

  • 2024/2025 4th module
    0.33 * Домашнее задание + 0.17 * Индивидуальный проект + 0.25 * Соревнование + 0.08 * Тест + 0.17 * Экзамен
Bibliography

Bibliography

Recommended Core Bibliography

  • A first course in machine learning, Rogers, S., 2012

Recommended Additional Bibliography

  • Network science, Barabasi, A.-L., 2019

Authors

  • Антропова Лариса Ивановна