Graph Neural Networks

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Lecture on Graph Neural Networks
Graph Neural Networks (GNNs) recently emerged to a powerful approach for representation learning on relational data such as social networks, molecular graphs or geometry. Similar to the concepts of convolutional and pooling layers on regular domains, GNNs are able to (hierarchically) extract localized embeddings by passing, transforming, and aggregating information between nodes. In this lecture, I will provide a broad overview of this highly active research field, and will cover relevant topics such as scalability and applications based on GNNs. In a hands-on session, you will implement and train GNNs from scratch using the PyTorch Geometric library [1]. You will learn how to apply GNNs to your own problems and how PyTorch Geometric enables high GPU throughput on highly sparse and irregular data of varying size. [1] github.com/rusty1s/pytorch_ge...

[@ronjarieger567]

your work is amazing! i read your masterthesis, which deserves more popularity. thanks for being so clever and helpful at the same time :D

[@davidkhassias4876]

Great work Matthias! Danke sehr

[@MatteoTaiana]

Thanks Matthias, very good lecture!

[@c.a.4073]

Great and inspiring work. Thank you very much for all of your efforts!

[@DavenH]

Awesome video, so informative and clear.

[@avishkarsaha8506]

Great introduction to GNNs, great lecture

[@jijie133]

Great video!

[@jacekc6179]

Vielen Dank.

[@N3Wk1d5]

1:19:52 : Code for simple GCN

[@mehmetaliozer2403]

what is the roadmap for gnn study, any recommendations, readlist ?

[@frun]

Is it sensible to use iterated functions for NNs?