Glad you like it. Now go and build crazy stuff!
As soon as I saw the title, my reaction was "YESSSS!"... A while ago, I was looking for something like this, but I soon realized that deep learning on graphs is still comparatively new, and not many python modules exist for it.
I'd love to tinker with this. What is a classic problem or demo for graph neural networks? Is there an equivalent to the MNIST problem for GNNs that would make a good learning exercise?
The official docs have some of the details: https://danielegrattarola.github.io/spektral/data/
More about Graph NNs: https://arxiv.org/pdf/1812.08434.pdf
Machine Learning in Complex Networks
Front Matter - https://link.springer.com/content/pdf/bfm%3A978-3-319-17290-...
Vectorize the adjacency matrix.
How is the data fed into the model? Are the graphs represented by a dictionary or an adjacency matrix (or neither)? Does anyone have more links I can read more about graph based deep learning?
YEEES I will finally be able to complete a college assignment that I postponed