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Yes, this is always something that I wanted to add, but never had the time to do so. I would very much appreciate an initiative to start adding generative models to the library. |
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I am not sure whether this is the correct channel to initiate discussion to should I use the GitHub issues!
I find Pytorch Geometric is easy to use and there are great examples for graph classification, matching i.e. predictive tasks. However, Pytorch Geometric lacks examples of generative models.
I am wondering whether there are any plans to include any of the following models:
Furthermore, Pytorch Geometric lacks examples of Graph2Seq based models. It would be awesome to include consider any of the following Graph2Seq based generative models.
Graph-to-Sequence Learning using Gated Graph Neural Networks, ACL’18
https://github.com/beckdaniel/acl2018_graph2seq
Using MXNet
Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning, ACL’19
https://github.com/Cartus/DCGCN
Using MXNet 1.3.0
Heterogeneous Graph Transformer for Graph-to-Sequence Learning, ACL’18
https://github.com/QAQ-v/HetGT
Using OpenNMT
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