Introduction to Graph Neural Networks

Graphs are universal data structures that can represent complex relational data. This course will explore and try to explain the most important modern graph neural networks and computational modules. We will learn how to build a GNN from scratch, walking through each part of the model and explain it. In the second part of the course, we will bring convolution to graph models, and we will explore Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Sample and Aggregate (GraphSAGE) and Graph Isomorphism Network (GIN). The course will also cover the challenges of using graphs in machine learning, how to overcome them and some empirical GNN design lessons.

Postgraduate

8

Short Course

REGISTRATION: Free of charge

Both AIDA and non-AIDA students are encouraged to participate in this short course.

If you are an AIDA Student* already, please:
Step (a): Register in the course, please send fill the REGISTRATION FORM.
AND
Step (b): Enroll in the same course in the AIDA system using the button below, so that this course enters your AIDA Certificate of Course Attendance.

If you are not an AIDA Student, do only step (a).
*AIDA Students should have been registered in the AIDA system already (they are PhD students or PostDocs that belong only to the AIDA Members listed in this page: Members)

3 Days

29-31 May, 12:00-14:00 GMT

English

online

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