.. _tutorials3-index:

Generative models
==================

* **DGMG** `[paper] <https://arxiv.org/abs/1803.03324>`__ `[tutorial]
  <3_generative_model/5_dgmg.html>`__ `[PyTorch code]
  <https://github.com/dmlc/dgl/tree/master/examples/pytorch/dgmg>`__:
  This model belongs to the family that deals with structural
  generation. Deep generative models of graphs (DGMG) uses a state-machine approach. 
  It is also very challenging because, unlike Tree-LSTM, every
  sample has a dynamic, probability-driven structure that is not available
  before training. You can progressively leverage intra- and
  inter-graph parallelism to steadily improve the performance.

* **JTNN** `[paper] <https://arxiv.org/abs/1802.04364>`__ `[PyTorch code]
  <https://github.com/dmlc/dgl/tree/master/examples/pytorch/jtnn>`__:
  This network generates molecular graphs using the framework of
  a variational auto-encoder. The junction tree neural network (JTNN) builds
  structure hierarchically. In the case of molecular graphs, it uses a junction tree as
  the middle scaffolding.