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authoredApr 2, 2020
[README] New stuffs from 0.4.3 (dmlc#1412)
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‎README.md

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@@ -18,7 +18,9 @@ DGL is an easy-to-use, high performance and scalable Python package for deep lea
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</p>
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2020
## <img src="http://data.dgl.ai/asset/image/new.png" width="30">DGL News
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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).
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*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).
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*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).
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## Using DGL
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Table: Training time(in seconds) for 200 epochs and memory consumption(GB)
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Here is another comparison of DGL on TensorFlow backend with other TF-based GNN tools (training time in seconds for one epoch):
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| Dateset | Model | DGL | GraphNet | tf_geometric |
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| ------- | ----- | --- | -------- | ------------ |
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| Core | GCN | 0.0148 | 0.0152 | 0.0192 |
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| Reddit | GCN | 0.1095 | OOM | OOM |
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| PubMed | GCN | 0.0156 | 0.0553 | 0.0185 |
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| PPI | GCN | 0.09 | 0.16 | 0.21 |
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| Cora | GAT | 0.0442 | n/a | 0.058 |
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| PPI | GAT | 0.398 | n/a | 0.752 |
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High memory utilization allows DGL to push the limit of single-GPU performance, as seen in below images.
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| <img src="http://data.dgl.ai/asset/image/DGLvsPyG-time1.png" width="400"> | <img src="http://data.dgl.ai/asset/image/DGLvsPyG-time2.png" width="400"> |
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| -------- | -------- |
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| Releases | Date | Features |
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|-----------|--------|-------------------------|
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| v0.4.3 | 03/31/2020 | - TensorFlow support <br> - DGL-KE <br> - DGL-LifeSci <br> - Heterograph sampling APIs (experimental) |
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| v0.4.2 | 01/24/2020 | - Heterograph support <br> - TensorFlow support (experimental) <br> - MXNet GNN modules <br> |
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| v0.3.1 | 08/23/2019 | - APIs for GNN modules <br> - Model zoo (DGL-Chem) <br> - New installation |
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| v0.2 | 03/09/2019 | - Graph sampling APIs <br> - Speed improvement |

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