Graph data is ubiquitous. Any system with entities and relationships between them can be represented as a graph. Over the past decade, machine learning algorithms have made remarkable progress in fields such as natural language processing, computer vision, and speech recognition. This success is primarily due to deep neural network architectures' ability to extract high-level features from Euclidean-structured data like images, text, and audio. However, graph data has not received the same level of attention.
In this course, we will explore how to create machine learning models to extract high-level features from graph data, a process known as graph representation learning. The topics covered in this course include graph neural networks (GNNs), such as graph convolutions and graph attention mechanisms, scalable GNNs for big data applications, spatiotemporal data analysis with GNNs, recommender systems, and graph generation. This course also includes laboratory sessions to provide hands-on experience with these concepts.
