/tutorials/models/3_generative_model

.. _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.