[README] New stuffs from 0.4.3 (dmlc#1412)

README.md

@@ -18,7 +18,9 @@ DGL is an easy-to-use, high performance and scalable Python package for deep lea
 </p>
 
 ## <img src="http://data.dgl.ai/asset/image/new.png" width="30">DGL News
-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
 
@@ -110,6 +112,17 @@ class GATLayer(nn.Module):
 
 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):
+
+| Dateset | Model | DGL | GraphNet | tf_geometric |
+| ------- | ----- | --- | -------- | ------------ |
+| Core | GCN | 0.0148 | 0.0152 | 0.0192 |
+| Reddit | GCN | 0.1095 | OOM | OOM |
+| PubMed | GCN | 0.0156 | 0.0553 | 0.0185 |
+| PPI | GCN | 0.09 | 0.16 | 0.21 |
+| Cora | GAT | 0.0442 | n/a | 0.058 |
+| PPI | GAT | 0.398 | n/a | 0.752 |
+
 High memory utilization allows DGL to push the limit of single-GPU performance, as seen in below images.
 | <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"> |
 | -------- | -------- |
@@ -190,6 +203,7 @@ Refer to the guide [here](https://docs.dgl.ai/install/index.html#install-from-so
 
 | Releases  | Date   | Features |
 |-----------|--------|-------------------------|
+| v0.4.3    | 03/31/2020 | - TensorFlow support <br> - DGL-KE <br> - DGL-LifeSci <br> - Heterograph sampling APIs (experimental) |
 | v0.4.2      | 01/24/2020 |  - Heterograph support <br> - TensorFlow support (experimental) <br> - MXNet GNN modules <br> | 
 | v0.3.1 | 08/23/2019 | - APIs for GNN modules <br> - Model zoo (DGL-Chem) <br> - New installation |
 | v0.2 | 03/09/2019 | - Graph sampling APIs <br> - Speed improvement |