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Found 3 papers, 2 papers with code
GraphNVP: an Invertible Flow-based Model for Generating Molecular Graphs
In contrast, our model is the first invertible model for the whole graph components: both of dequantized node attributes and adjacency tensor are converted into latent vectors through two novel invertible flows.
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model.
BayesGrad: Explaining Predictions of Graph Convolutional Networks
A possible approach to answer this question is to visualize evidence substructures responsible for the predictions.
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