dmlc--dgl
Hongzhi (Steve), Chen
dce899190e
* auto-reformat * lintrunner --------- Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>
.. _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.