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Should be able to extract the weighted edges from the model and make a visualization
Show a well performing example and a poorly performing example - This can also be used for subgraphs in the future when subgraph based explanations are set up.
Complexity comes from the 3 planes of the output - Need to be able to show all of them on one image without it becoming too cluttered
Impliment gnn-lrp on nugraph - work into the abstract class
reference paper - https://arxiv.org/pdf/2006.03589.pdf
reference paper - https://arxiv.org/pdf/1903.03894.pdf
integrate into existing framework
Impliment XGNN on NuGraph - include visuals and local inference outputs.
Reference paper - https://dl.acm.org/doi/pdf/10.1145/3394486.3403085
Should be usable with the module from #1
Edges in the graph are not bidirectional - it just draws them twice.
All drawing programs and edge masking should mask both directions or only optimize over one direction.
Probably solvable with changing all the edges to sets but hey
When a subgraph is displayed, a ghost
of the whole graph should be under the subgraph.
There should also be a uniform axis (x,y) for all subgraphs of the same graph. It'll make it easier to compare which parts of the graph compose the subgraph.
(Only one or two graphs?)
After discussion in the workshop - it would be advantageous to be able to monitor the message passing after each iteration
Explanations expect network to output a single class (or the probabilities for all classes) - so the output scheme of nugraph does not work automatically. There are a few possible solutions to this
Second is way easier but the first is more rigorous and would give better results (most likely?) - it's worth exploring I'd say.
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