Closed nashid closed 3 years ago
From now on, we recommend using our discussion forum (https://github.com/rusty1s/pytorch_geometric/discussions) for proposing new features.
Generative Model
Pytorch Geometric is easy to use and there are some great examples. However, Pytorch Geometric lacks examples of generative models.
I am wondering whether there are any plans to include any of the following models:
Also, Pytorch Geometric lacks examples of Graph2Seq based models. It would be awesome to 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
I'm happy to contribute as well.
See https://github.com/rusty1s/pytorch_geometric/discussions/2614
rusty1s
commented
3 years ago
From now on, we recommend using our discussion forum (https://github.com/rusty1s/pytorch_geometric/discussions) for proposing new features.
🚀 Feature
Generative Model
Motivation
Pytorch Geometric is easy to use and there are some great examples. However, Pytorch Geometric lacks examples of generative models.
I am wondering whether there are any plans to include any of the following models:
Also, Pytorch Geometric lacks examples of Graph2Seq based models. It would be awesome to 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
Additional context
I'm happy to contribute as well.