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There are graph neural networks (meaning NNs that work on graphs), but I don’t think that’s what is used here.
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I do not understand what you mean by “routes”. I suspect that you have misunderstood something fundamental.
- I’m not talking about that. What’s weights, biases and shape if not a graph?
- By routes, I mean that the path of the graph doesn’t necessarily converge and that it is often more tree-like.
You can see a neural net as a graph in that the neurons are connected nodes. I don’t believe that graph theory is very helpful, though. The weights are parameters in a system of linear equations; the numbers in a matrix/tensor. That’s not how the term is used in graph theory, AFAIK.
ETA: What you say about “routes” (=paths?) is something that I can only make sense of, if I assume that you misunderstood something. Else, I simply don’t know what that is talking about.
If you look at the nodes which are most likely to trigger from given inputs then you can draw paths
