Examples of Generative Model

## 🚀 Feature
Support for Generative Models

## Motivation
**DGL** is intuitive to use and there are some great examples. However, **DGL** lacks generative models.

I am wondering whether there are any plans to include any of the following models:

- GraphRNN: https://github.com/JiaxuanYou/graph-generation

Also, **DGL** 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

- Gated Graph Sequence Neural Networks, Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel.

- Heterogeneous Graph Transformer for Graph-to-Sequence Learning, ACL’18
https://github.com/QAQ-v/HetGT

## Alternatives
I am considering the following GGNN implementation from OpenNMT.

OpenNMT GGNN
https://opennmt.net/OpenNMT-py/examples/GGNN.html

## Pitch

DGL is easy to use and mostly for predictive tasks. Supporting more generative models would clearly increase its adoption.