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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

Analysis and Visualization of Networks

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

Instructor

Karpov, Ilia

Karpov, Ilia

Course Syllabus

Full SyllabusAsk Question

Abstract

This course introduces methods and algorithms for analysing and visualizing graphs and networks. The course includes a review of modern network analysis and visualization techniques with their applications in various domains. We will concern on three main topics: network analysis methods based on applied graph theory, graph drawing algorithms, applications of network analysis and visualization to real problems.
Learning Objectives

Learning Objectives

  • To know the classification of main network analysis tasks, basic methods and algorithms, most popular software tools.
  • To be able to define a graph-theoretic description of network analysis task and corresponding network visualization requirements.
  • To be able to select reasonably an appropriate project solutions and tools for network analysis workflow.
  • To be able to develop a new variants of graph drawing algorithms.
Expected Learning Outcomes

Expected Learning Outcomes

  • Students design and solve graph-theoretical mathematical models.
  • Students know the basic concepts of analysing and visualizing graphs and networks.
  • Students select and justify appropriate graph drawing method and algorithm.
  • Students use development techniques, skills and tools necessary to network visualization.
Course Contents

Course Contents

  • Introduction
  • Graphs, topology and geometry
  • Visualization of small graphs: drawing and layout
  • Visualization of large graphs
  • Interactive visualization of graphs
  • Visualization of graphs and networks in real world applications
  • Modern trends in graph databases and network analysis software
Assessment Elements

Assessment Elements

  • non-blocking Quizzes (tests)
    Quiz 1-9
  • non-blocking Homeworks
Interim Assessment

Interim Assessment

  • 2024/2025 3rd module
    0.6 * Homeworks + 0.4 * Quizzes (tests)
Bibliography

Bibliography

Recommended Core Bibliography

  • Brath, R., Jonker, D. Graph Analysis and Visualization: Discovering Business Opportunity in Linked Data. – Wiley, 2015. – 513 pp.

Recommended Additional Bibliography

  • Newman, M., Watts, D. J., and Barabási, A. The Structure and Dynamics of Networks. – Princeton University Press, 2006. – 592 pp.

Authors

  • Антропова Лариса Ивановна
  • Karpov Ilia Andreevich