.. _tutorials3-index:

Generative models
------------------------------

* **DGMG** `[paper] <https://arxiv.org/abs/1803.03324>`__ `[tutorial] <models/5_dgmg.html>`__
  `[code] <https://github.com/jermainewang/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 (wip)]`: 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.