Департамент анализа данных и искусственного интеллекта

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Департамент анализа данных и искусственного интеллекта был создан в 2014 году на базе кафедры анализа данных и искусственного интеллекта. В его состав входят исследователи с мировым именем, активно участвующие в международных исследовательских проектах.

Публикации

Статья

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.

Статья

Generalized relational tensors for chaotic time series

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

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

Статья

Language models for some extensions of the Lambek calculus

Kanovich M., Kuznetsov S., Scedrov A.

Information and Computation. 2022. Vol. 287.

Статья

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.

Глава в книге

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.

Глава в книге

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.

Глава в книге

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.

Статья

Human knowledge models: Learning applied knowledge from the data

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

Plos One. 2022. Vol. 17. No. 10.

Все публикации

Кто читает:: Департамент анализа данных и искусственного интеллекта

Статус:: Курс по выбору

Когда читается:: 4-й курс, 3 модуль

Преподаватель

Карпов Илья Андреевич

Full Syllabus Ask 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

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

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

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

Quizzes (tests)
Quiz 1-9
Homeworks

Interim Assessment

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

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

Analysis and Visualization of Networks

Преподаватель

Course Syllabus

Abstract

Learning Objectives

Expected Learning Outcomes

Course Contents

Assessment Elements

Interim Assessment

Bibliography

Recommended Core Bibliography

Recommended Additional Bibliography

Authors