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Resources that was very useful for me when learning about GNNs that you can check out for more information and from which I've used in the slides:

Cs224w:    • Stanford CS224W: Machine Learning wit...  
https://distill.pub/2021/gnn-intro/
https://distill.pub/2021/understandin...
   • Graph Neural Networks  
   • Intro to graph neural networks (ML Te...  
   • Theoretical Foundations of Graph Neur...  
   • ICLR 2021 Keynote - "Geometric Deep L...  

Paid Courses I recommend for learning (affiliate links, no extra cost for you):
⭐ Machine Learning Specialization https://bit.ly/3hjTBBt
⭐ Deep Learning Specialization https://bit.ly/3YcUkoI
📘 MLOps Specialization http://bit.ly/3wibaWy
📘 GAN Specialization https://bit.ly/3FmnZDl
📘 NLP Specialization http://bit.ly/3GXoQuP

✨ Free Resources that are great:
NLP: https://web.stanford.edu/class/cs224n/
CV: http://cs231n.stanford.edu/
Deployment: https://fullstackdeeplearning.com/
FastAI: https://www.fast.ai/

💻 My Deep Learning Setup and Recording Setup:
https://www.amazon.com/shop/aladdinpe...

GitHub Repository:
https://github.com/aladdinpersson/Mac...

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▶️ You Can Connect with me on:
Twitter -   / aladdinpersson  
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Github - https://github.com/aladdinpersson

Timestamps:
0:00 Introduction
1:24 Why graphs
4:13 What is a graph
7:06 Common graph tasks
11:08 Representation of a graph
12:46 - How does a GNN work?
14:35 - Understanding information propagation
17:24 - Key property: Permutation Invariance
19:33 - Key property: Permutation Equivariance
22:22 - Message passing computation
23:53 - GNN Variant: Convolution
26:37 - GNN Variant: Attention
28:39 - Ending