DGL release and change logs ========== Refer to the roadmap issue for the on-going versions and features. 0.2 --- Major release that includes many features, bugfix and performance improvement. Speed of GCN model on Pubmed dataset has been improved by **4.19x**! Speed of RGCN model on Mutag dataset has been improved by **7.35x**! Important new feature: **graph sampling APIs**. Update details: # Model examples - [x] TreeLSTM w/ MXNet (PR #279 by @szha ) - [x] GraphSage (@ZiyueHuang ) - [x] Improve GAT model speed (PR #348 by @jermainewang ) # Core system improvement - [x] Immutable CSR graph structure (PR #342 by @zheng-da ) - [x] Finish remaining functionality (Issue #369, PR #404 by @yzh119) - [x] Nodeflow data structure (PR #361 by @zheng-da ) - [x] Neighbor sampler (PR #322 ) - [x] Layer-wise sampler (PR #362 by @GaiYu0 ) - [x] Multi-GPU support by data parallelism (PR #356 #338 by @ylfdq1118 ) - [x] More dataset: - [x] Reddit dataset loader (PR #372 by @ZiyueHuang ) - [x] PPI dataset loader (PR #395 by @sufeidechabei ) - [x] Mini graph classification dataset (PR #364 by @mufeili ) - [x] NN modules (PR #406 by @jermainewang @mufeili) - [x] GraphConv layer - [x] Edge softmax layer - [x] Edge group apply API (PR #358 by @VoVAllen ) - [x] Reversed graph and transform.py module (PR #331 by @mufeili ) - [x] Max readout (PR #341 by @mufeili ) - [x] Random walk APIs (PR #392 by @BarclayII ) # Tutorial/Blog - [x] Batched graph classification in DGL (PR #360 by @mufeili ) - [x] Understanding GAT (@sufeidechabei ) # Project improvement - [x] Python lint check (PR #330 by @jermainewang ) - [x] Win CI (PR #324 by @BarclayII ) - [x] Auto doc build (by @VoVAllen ) - [x] Unify tests for different backends (PR #333 by @BarclayII ) 0.1.3 ----- Bug fix * Compatible with Pytorch v1.0 * Bug fix in networkx graph conversion. 0.1.2 ----- First open release. * Basic graph APIs. * Basic message passing APIs. * Pytorch backend. * MXNet backend. * Optimization using SPMV. * Model examples w/ Pytorch: - GCN - GAT - JTNN - DGMG - Capsule - LGNN - RGCN - Transformer - TreeLSTM * Model examples w/ MXNet: - GCN - GAT - RGCN - SSE