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Machine Learning with Graphs - Part 1
Machine Learning with Graphs - from graph data and graph representations to graph neural networks.

Graphs provide versatile abstraction for modelling, visualising and analysing rich and heterogeneous datasets. They offer a fundamental toolbox for modelling social, technological, biological systems and more.

In this course, we will focus on the challenges and opportunities specific to the analysis of large graphs. The goals is to introduce students to the computational, algorithmic, and modelling foundations of the burgeoning area of machine learning ton graphs. We will study algorithms, programming frameworks and tools, and applications.

We will cover areas such as node and edge embeddings, representation learning and Graph Neural Networks; algorithms for the web, Knowledge Graphs, social network analysis, biological networks.

Part 1 of the course consists of three lectures:
Lecture 1: 16/03/21 - Challenges and Opportunities
Lecture 2: 23/03/21 - Shallow Graph Machine Learning
Lecture 3: 30/03/21 - Deep Graph Machine Learning

Part 2 of the course consists of two seminars that you can register for here:
https://lse.zoom.us/meeting/register/tZElceqhqzIuGtFcOxRXylbwahgG0h8aX-6u

For more information, please visit:
https://sites.google.com/view/lsegraphrepresentations/home?authuser=0
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