In my application, I have inputs and outputs that could be represented as graphs. I have a number of acceptable pairs of input and output graphs. I want to use these to train a model.
I am looking for pointers where simple examples of learning methods with graphs as input are discussed. Please note that the graph size is not fixed.
A sample input is
Graph: Node A: Component X with parameter size = 12 Node B: Component Y with parameter size = 30 Node C: Component Y with parameter size = 30 A connects to B A connects to C
Node A: x=0, y=0 Node B: x=-21, y=0 Node C: x=21, y=0
In this case, we expect the model to understand that input graph is symmetric and a particular way of arranging them is preferred. We want to train the model over a large set of such input-output pairs and then use it to generate output on new inputs.