I was reading the following article on Towards Data Science (here) and it says the following, regarding the calculation of convolutional layers:
So the overall steps are:
- Transform the graph into the spectral domain using eigendecomposition
- Apply eigendecomposition to the specified kernel
- Multiply the spectral graph and spectral kernel (like vanilla convolutions)
- Return results in the original spatial domain (analogous to inverse GFT)
Question: How can we visualize the convolutional layer working for a graph neural network?
For example, for a CNN we can imagine the following (source: Stanford CS231n YouTube lectures, Lecture 5: Convolutional Neural Networks (here)). What is the analogous image for a graph convolutional filter?