At my work, we're currently doing some research into data visualisation for highly interconnected data, basically graphs.
We've been implementing all sorts of different layouts and trying to see which fits best, but, due to the nature of the problem --it's a visual thing - we needed to come up with some automated way to analyse the result so welcome up with a bunch of metrics to analyse our layouts.
So far, the most important metrics have been information density, edge crossings, node overlap and edge length. This gives us some good results and has allowed us to fine-tune our layout algorithms.
However, when a new graph is loaded, we noticed that humans still tend to fiddle a lot with the structure of the layout. Moreover, it seems that our metrics do a good job of predicting where a user is likely to mess around. Graph layout is a tough problem, so after some discussion, the idea of just throwing data at a neural network and let it figure it out came up.
None of us are experts, or even experienced in AI. I'm the one with the most contact with AI methods. All I've ever done were simple NN models, no convolution, feedback or feedforward or anything of the sorts, but it seems to me this should be doable.
Maybe it's my lack of expertise here, but I haven't been able to find any good information on this sort of application for NNs, so I was hoping someone here could point me in the right direction.
- What sort of model is best for such a situation? and why? Is this actually possible or would it be super complicated? Has anyone ever tried something like this before?
If it helps, our input data (for v1, I guess) would be two arrays of variable length, one for the nodes and another for the relationships between them and the output data would be an array with the node XY coordinates.