Graph Neural Networks

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.

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.

no entry requirements

• 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

neural network, graph, embedding