/tutorials/models/3_generative_model

.. _tutorials3-index:



Generative models

==================



* **DGMG** `[paper] <https://arxiv.org/abs/1803.03324>`__ `[tutorial]

  <3_generative_model/5_dgmg.html>`__ `[code]

  <https://github.com/dmlc/dgl/tree/master/examples/pytorch/dgmg>`__:

  this model belongs to the important family that deals with structural

  generation. DGMG is interesting because its state-machine approach is the

  most general. It is also very challenging because, unlike Tree-LSTM, every

  sample has a dynamic, probability-driven structure that is not available

  before training. We are able to progressively leverage intra- and

  inter-graph parallelism to steadily improve the performance.



* **JTNN** `[paper] <https://arxiv.org/abs/1802.04364>`__ `[code]

  <https://github.com/dmlc/dgl/tree/master/examples/pytorch/jtnn>`__:

  unlike DGMG, this paper generates molecular graphs using the framework of

  variational auto-encoder. Perhaps more interesting is its approach to build

  structure hierarchically, in the case of molecular, with junction tree as

  the middle scaffolding.