I have two closely related points regarding the weight sharing and generalization of graph Neural network. For illustration purposes, I attached two images which I reference. Images are taken from the Stanford course "CS224W:Machine Learning with Graphs" given by "Jure Leskovec"
- In the centre above one can see that the blue, red and green node feed into the yellow one. The same can be seen one the right side with the same colour configuration. Are now these parameters shared across all computational graphs which 3 nodes feeding into 1?
- If yes, then how does this generalize to a potential new graph? In the second image above, one can see that there are new types of computational graphs needed, e.g. on the right side the orange node has five neighbours while there is no such node on the left side. So, how does the generalization work in this case?