Graph Neural Networks

Garant předmětu:

Lecturer:

Tutor:

Supervisor:

Department of Applied Mathematics

Synopsis:

The course introduces students to advanced artificial intelligence techniques for working with graphs. Lectures will focus on the latest graph neural networks for creating vector representations of nodes, edges and entire graphs. The techniques discussed cover various types of graphs, including time-varying graphs. The last part of the course also covers graph generation and interpretability of graph neural networks. In the exercises, students will try out selected techniques and problems.

Requirements:

no entry requirements

Syllabus of lectures:

1) Introduction to the subject, motivation and definition of terms.

2) Representations based on the adjacency matrix and random walks through the graph.

3) Convolutional graph neural networks.

4) Representations of time-variable graphs.

5) Graph generation and representation using graph autoencoders.

6) Interpretability and applications in natural language processing and recommender systems.

Syllabus of tutorials:

1) Introduction to the StellarGraph library.

2) Vector representation of graphs.

3) Classification and clustering of nodes and graphs.

4) Graphs with time component.

5) Working on a semestral project.

6) Submission of the project and its presentation.

Study Objective:

Study materials:

Deep Learning; I. Goodfellow, Y. Bengio, A. Courville; MIT Press; 2016; ISBN 978-0262035613.

Introduction to Graph Neural Networks; Zhiyuan Liu, Jie Zhou; Morgan & Claypool Publishers; 2020; ISBN-13‎ 978-1681737652

Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning); William L. Hamilton; Morgan & Claypool Publishers; 2020; ISBN‎ 978-1681739632

Heterogeneous Graph Representation Learning and Applications; Chuan Shi, Xiao Wang, Philip S. Yu; Springer; 2022; ISBN:‎ 978-9811661655

Note:

Further information:

https://courses.fit.cvut.cz/NI-GNN/

No time-table has been prepared for this course

The course is a part of the following study plans:

• Master specialization Computer Science, in Czech, 2018-2019 (elective course)
• Master specialization Computer Security, in Czech, 2020 (elective course)
• Master specialization Design and Programming of Embedded Systems, in Czech, 2020 (elective course)
• Master specialization Computer Systems and Networks, in Czech, 202 (elective course)
• Master specialization Management Informatics, in Czech, 2020 (elective course)
• Master specialization Software Engineering, in Czech, 2020 (elective course)
• Master specialization System Programming, in Czech, version from 2020 (elective course)
• Master specialization Web Engineering, in Czech, 2020 (elective course)
• Master specialization Knowledge Engineering, in Czech, 2020 (elective course)
• Master specialization Computer Science, in Czech, 2020 (elective course)
• Mgr. programme, for the phase of study without specialisation, ver. for 2020 and higher (elective course)
• Study plan for Ukrainian refugees (elective course)
• Master specialization System Programming, in Czech, version from 2023 (elective course)
• Master specialization Computer Science, in Czech, 2023 (elective course)