That is true, but unfortunately "out of the box", they're not well suited just be "fed into" an NN. Even if you think of the adjacency matrix as very similar to how the weights are laid out in a feed-forward neural network, you can't ignore that:
- in real life, graphs are not fixed
- you need to deal with the many different potential representations of the graph (permutation invariance)
- the nodes are usually containing more features than a single scalar value
but this is definitely not the best explanation, I think this guy does a lot better job: https://youtu.be/JtDgmmQ60x8
- in real life, graphs are not fixed
- you need to deal with the many different potential representations of the graph (permutation invariance)
- the nodes are usually containing more features than a single scalar value
but this is definitely not the best explanation, I think this guy does a lot better job: https://youtu.be/JtDgmmQ60x8