.. _sphx_glr_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. .. raw:: html <div class="sphx-glr-thumbnails"> .. thumbnail-parent-div-open .. raw:: html <div class="sphx-glr-thumbcontainer" tooltip="Author: Mufei Li, Lingfan Yu, Zheng Zhang"> .. only:: html .. image:: /tutorials/models/3_generative_model/images/thumb/sphx_glr_5_dgmg_thumb.png :alt: :ref:`sphx_glr_tutorials_models_3_generative_model_5_dgmg.py` .. raw:: html <div class="sphx-glr-thumbnail-title">Generative Models of Graphs</div> </div> .. thumbnail-parent-div-close .. raw:: html </div> .. toctree:: :hidden: /tutorials/models/3_generative_model/5_dgmg