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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025

Graph Neural Networks

The course is not on the list Without time-table
Code Completion Credits Range Language
NI-GNN Z,ZK 4 1P+1C Czech
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:
Data valid to 2024-04-11
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