03/02/2020: **Check out this cool paper: [Benchmarking Graph Neural Networks](https://arxiv.org/abs/2003.00982)!** It includes a DGL-based benchmark framework for novel medium-scale graph datasets, covering mathematical modeling, computer vision, chemistry and combinatorial problems. See [repo here](https://github.com/graphdeeplearning/benchmarking-gnns).
*03/31/2020*: The new **v0.4.3 release** includes official TensorFlow support, with 15 popular GNN modules. DGL-KE and DGL-LifeSci, two packages for knowledge graph embedding and chemi- and bio-informatics respectively, have graduated as standalone packages and can be installed by pip and conda. The new release provides full support of graph sampling on heterogeneous graphs, with multi-GPU acceleration. See our [new feature walkthrough](https://www.dgl.ai/release/2020/04/01/release.html) and [release note](https://github.com/dmlc/dgl/releases/tag/0.4.3).
*03/02/2020*: **Check out this cool paper: [Benchmarking Graph Neural Networks](https://arxiv.org/abs/2003.00982)!** It includes a DGL-based benchmark framework for novel medium-scale graph datasets, covering mathematical modeling, computer vision, chemistry and combinatorial problems. See [repo here](https://github.com/graphdeeplearning/benchmarking-gnns).
## Using DGL
## Using DGL
...
@@ -110,6 +112,17 @@ class GATLayer(nn.Module):
...
@@ -110,6 +112,17 @@ class GATLayer(nn.Module):
Table: Training time(in seconds) for 200 epochs and memory consumption(GB)
Table: Training time(in seconds) for 200 epochs and memory consumption(GB)
Here is another comparison of DGL on TensorFlow backend with other TF-based GNN tools (training time in seconds for one epoch):
