I published a multipart “Tutorial on Graph Neural Networks for Computer Vision and Beyond” starting from some basics [1], then an overview explaining several important methods [2] and a separate post on spectral convolution [3].

I know there are a lot of blog posts on graph networks already, but in my tutorial I tried to explain key (and sometimes complicated) ideas in very simple terms from a computer vision perspective, so it should be good for those with a computer vision and machine learning background. I provide detailed Python and PyTorch examples to clarify differences between methods.

I wasn’t sure if to publish it here due to this discussion (Regarding beginner’s guides: https://www.reddit.com/r/MachineLearning/comments/co37ut/regarding_beginners_guides/ ), but hopefully it will be appreciated here. Otherwise, feel free to downvote or remove.

Any questions or feedback is very welcome, especially, if you notice some mistakes or confusing info.

[1] Part 1 of the Tutorial: convolution on graphs and differences between simple fully-connected neural networks (MLPs) and graph networks: https://medium.com/@BorisAKnyazev/tutorial-on-graph-neural-networks-for-computer-vision-and-beyond-part-1-3d9fada3b80d

[2] Anisotropic, Dynamic, Spectral and Multiscale Filters Defined on Graphs: https://towardsdatascience.com/tutorial-on-graph-neural-networks-for-computer-vision-and-beyond-part-2-be6d71d70f49

[3] Spectral Graph Convolution Explained and Implemented Step By Step: https://towardsdatascience.com/spectral-graph-convolution-explained-and-implemented-step-by-step-2e495b57f801