0.2

Major release that includes many features, bugfix and performance improvement. Speed of GCN model on Pubmed dataset has been improved by 4.32x! Speed of RGCN model on Mutag dataset has been improved by 3.59x! Important new feature: graph sampling APIs.

Update details:

Model examples

• TreeLSTM w/ MXNet (PR #279 by @szha )
• GraphSage (@ZiyueHuang )
• Improve GAT model speed (PR #348 by @jermainewang )

Core system improvement

• SPMM performance improvement (PR #427 by @ylfdq1118 )
  • Replacing torch.spmm with torch.index_select plus torch.scatter_add gives a huge boost in the speed. (inspired by pytorch-geometric project).
• Immutable CSR graph structure (PR #342 by @zheng-da )
  • Finish remaining functionality (Issue #369, PR #404 by @yzh119)
• Nodeflow data structure (PR #361 by @zheng-da )
• Neighbor sampler (PR #322 )
• Layer-wise sampler (PR #362 by @GaiYu0 )
• Multi-GPU support by data parallelism (PR #356 #338 by @ylfdq1118 )
• More dataset:
  • Reddit dataset loader (PR #372 by @ZiyueHuang )
  • PPI dataset loader (PR #395 by @sufeidechabei )
  • Mini graph classification dataset (PR #364 by @mufeili )
• NN modules (PR #406 by @jermainewang @mufeili)
  • GraphConv layer
  • Edge softmax layer
• Edge group apply API (PR #358 by @VoVAllen )
• Reversed graph and transform.py module (PR #331 by @mufeili )
• Max readout (PR #341 by @mufeili )
• Random walk APIs (PR #392 by @BarclayII )

Tutorial/Blog

• Batched graph classification in DGL (PR #360 by @mufeili )
• Understanding GAT (@sufeidechabei )

Project improvement

• Python lint check (PR #330 by @jermainewang )
• Win CI (PR #324 by @BarclayII )
• Auto doc build (by @VoVAllen )
• Unify tests for different backends (PR #333 by @BarclayII )