[Doc] Implementation Index Page (#2443)

* Update

* Update

* Update

* Update README.md

* Update README.md

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

README.md

@@ -8,8 +8,9 @@
 [![Benchmark by ASV](http://img.shields.io/badge/benchmarked%20by-asv-green.svg?style=flat)](https://asv.dgl.ai/)
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](./LICENSE)
 
-Documentation ([Latest](https://docs.dgl.ai/en/latest/) | [Stable](https://docs.dgl.ai)) | [DGL at a glance](https://docs.dgl.ai/tutorials/basics/1_first.html#sphx-glr-tutorials-basics-1-first-py) | [Model Tutorials](https://docs.dgl.ai/tutorials/models/index.html) | [Discussion Forum](https://discuss.dgl.ai) | [Slack Channel](https://join.slack.com/t/deep-graph-library/shared_invite/zt-eb4ict1g-xcg3PhZAFAB8p6dtKuP6xQ)
+Documentation ([Latest](https://docs.dgl.ai/en/latest/) | [Stable](https://docs.dgl.ai)) | [DGL at a glance](https://docs.dgl.ai/tutorials/basics/1_first.html#sphx-glr-tutorials-basics-1-first-py) | [Model Tutorials](https://docs.dgl.ai/tutorials/models/index.html) | [Official Examples](examples/README.md) | [Discussion Forum](https://discuss.dgl.ai) | [Slack Channel](https://join.slack.com/t/deep-graph-library/shared_invite/zt-eb4ict1g-xcg3PhZAFAB8p6dtKuP6xQ)
 
+**For a full list of official DGL examples, see [here](examples).**
 
 DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow.
 

examples/pytorch/README.md

-# Model Examples using DGL (w/ Pytorch backend)
-
-Each model is hosted in their own folders. Please read their README.md to see how to
-run them.
-
-To understand step-by-step how these models are implemented in DGL. Check out our
-[tutorials](https://docs.dgl.ai/tutorials/models/index.html)
-
-## Model summary
-
-Here is a summary of the model accuracy and training speed. Our testbed is Amazon EC2 p3.2x instance (w/ V100 GPU).
-
-| Model | Reported <br> Accuracy | DGL <br> Accuracy | Author's training speed (epoch time) | DGL speed (epoch time) | Improvement |
-| ----- | ----------------- | ------------ | ------------------------------------ | ---------------------- | ----------- |
-| [GCN](https://arxiv.org/abs/1609.02907)  | 81.5% | 81.0% | [0.0051s (TF)](https://github.com/tkipf/gcn) | 0.0031s | 1.64x |
-| [GAT](https://arxiv.org/abs/1710.10903)  | 83.0% | 83.9% | [0.0982s (TF)](https://github.com/PetarV-/GAT) | 0.0113s | 8.69x |
-| [SGC](https://arxiv.org/abs/1902.07153) | 81.0% | 81.9% | n/a | 0.0008s | n/a |
-| [TreeLSTM](http://arxiv.org/abs/1503.00075) | 51.0% | 51.72% | [14.02s (DyNet)](https://github.com/clab/dynet/tree/master/examples/treelstm) | 3.18s | 4.3x |
-| [R-GCN <br> (classification)](https://arxiv.org/abs/1703.06103) | 73.23% | 73.53% | [0.2853s (Theano)](https://github.com/tkipf/relational-gcn) | 0.0075s | 38.2x |
-| [R-GCN <br> (link prediction)](https://arxiv.org/abs/1703.06103) | 0.158 | 0.151 | [2.204s (TF)](https://github.com/MichSchli/RelationPrediction) | 0.453s | 4.86x |
-| [JTNN](https://arxiv.org/abs/1802.04364) | 96.44% | 96.44% | [1826s (Pytorch)](https://github.com/wengong-jin/icml18-jtnn) | 743s | 2.5x |
-| [LGNN](https://arxiv.org/abs/1705.08415) | 94% | 94% | n/a | 1.45s | n/a |
-| [DGMG](https://arxiv.org/pdf/1803.03324.pdf) | 84% | 90% | n/a | 238s | n/a |
-| [GraphWriter](https://www.aclweb.org/anthology/N19-1238.pdf) | 14.31(BLEU) | 14.3(BLEU) | 1970s | 1192s | 1.65x |