GeometricFlux.jl (Convolutional Layers · GeometricFlux.jl) and GraphNeuralNetworks.jl (Convolutional Layers · GraphNeuralNetworks.jl) both implement a layer based on the Crystal Graph Convolutional Neural Network (CGCNN)(Phys. Rev. Lett. 120, 145301 (2018) - Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties).

The CGCNN architecture is usually defined over a multi-graph, where a pair of nodes might have multiple connecting edges, and each edge has a different encoding vector. Is this input datatype supported in current Julia graph network packages; or, failing that, is it easily implemented? Light searching has revealed a separate Multigraph.jl package (GitHub - QuantumBFS/Multigraphs.jl: A multigraph extension of LightGraphs.jl) that is not (perhaps?) integrated into the general Graphs ecsystem.

It’s not obvious that the `GNNGraph`

type (GraphNeuralNetworks.jl/gnngraph.jl at master · CarloLucibello/GraphNeuralNetworks.jl · GitHub) in GraphNeuralNetworks.jl supports multigraphs, but I might be missing something obvious.