Overview

This is an advanced course on machine learning with graph-structured data, focusing on the recent advances in the field of graph representation learning. The goal is to provide a systematic coverage of the fundamentals and foundations of graph representation learning. The course will introduce the definitions of the relevant machine learning models (e.g., graph neural networks), discuss their mathematical underpinnings, formally study their properties (e.g., relational inductive bias, expressive power), and demonstrate ways to effectively develop and train such models.

Learning outcomes

After studying this course, students will:

• Have knowledge of the different paradigms for performing graph machine learning.
• Understand the definition of a range of neural network models, along with their properties.
• Be able to derive and implement optimisation algorithms for these models.
• Understand the foundations of the Bayesian approach to graph machine learning.
• Understand how to choose a model to describe a particular type of data.
• Know how to evaluate a learned model in practice.
• Understand the mathematics necessary for constructing novel machine learning solutions.
• Be able to design and implement various graph machine learning algorithms in a range of real-world applications.

Prerequisites

Required background knowledge includes probability theory, linear algebra, continuous mathematics, multivariate calculus, and a basic understanding of graph theory and logic.  Students are required to have already taken a machine learning course. Good programming skills are needed, and lecture examples and practicals will be given mainly in Python and PyTorch.

Synopsis

Introduction

• Lecture 1: Overview and applications of graph learning

Shallow embeddings

• Lecture 2: Node embeddings
• Lecture 3: Knowledge graph embeddings