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: https://youtube.com/playlist?l....ist=PLoROMvodv4rPLKx
https://distill.pub/2021/gnn-intro/
https://distill.pub/2021/understanding-gnns/
https://www.youtube.com/playli....st?list=PLV8yxwGOxvv
https://youtu.be/8owQBFAHw7E
https://youtu.be/uF53xsT7mjc
https://youtu.be/w6Pw4MOzMuo

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

Graph machine learning has become very popular in recent years in the machine learning and engineering communities. In this video, we explore the math behind some of the most popular graph neural network algorithms!

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Other Videos in this Series
Why use graphs for machine learning? https://youtu.be/mu1Inz3ltlo
Intro to graph neural networks https://youtu.be/cka4Fa4TTI4
Spatio-Temporal Graph Neural Networks https://youtu.be/RRMU8kJH60Q

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In this course we implement the most popular Machine Learning algorithms from scratch using only Python and NumPy.

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Timeline:
00:00​ - Introduction
00:56​ - 1 KNN
23:00​ - 2 Linear Regression
43:38​ - 3 Logistic Regression
1:00:50​ - 4 Regression Refactoring
1:08:25​ - 5 Naive Bayes
1:29:11​ - 6 Perceptron
1:47:01​ - 7 SVM
2:06:33​ - 8 Decision Tree Part 1
2:17:12​ - 9 Decision Tree Part 2
2:48:00​ - 10 Random Forest
3:01:22​ - 11 PCA
3:18:43​ - 12 K-Means
3:48:15​ - 13 AdaBoost
4:15:53​ - 14 LDA
4:38:10​ - 15 Load Data From CSV

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