[Example][Refactor] Refactor GCN example (dmlc#4160)

* Refactor GCN example

* Refactor GCN based on graphsage

* Readme update

* Minor update

* update

* Remove user-defined GCN implementation

* README update

* Update

* Update CONTRIBUTORS.md

* update task_example_test

Co-authored-by: Xin Yao <[email protected]>

CONTRIBUTORS.md

@@ -63,3 +63,4 @@ Contributors
 * [Shaked Brody](https://github.com/shakedbr) from Technion
 * [Jiahui Liu](https://github.com/paoxiaode) from Nvidia
 * [Neil Dickson](https://github.com/ndickson-nvidia) from Nvidia
+* [Chang Liu](https://github.com/chang-l) from Nvidia

examples/pytorch/gcn/README.md

@@ -2,33 +2,21 @@ Graph Convolutional Networks (GCN)
 ============
 
 - Paper link: [https://arxiv.org/abs/1609.02907](https://arxiv.org/abs/1609.02907)
-- Author's code repo: [https://github.com/tkipf/gcn](https://github.com/tkipf/gcn). Note that the original code is 
-implemented with Tensorflow for the paper. 
+- Author's code repo: [https://github.com/tkipf/gcn](https://github.com/tkipf/gcn).
 
-Dependencies
-------------
-- PyTorch 0.4.1+
-- requests
-
-``bash
-pip install torch requests
-``
-
-Codes
------
-The folder contains three implementations of GCN:
-- `gcn.py` uses DGL's predefined graph convolution module.
-- `gcn_mp.py` uses user-defined message and reduce functions.
-Modify `train.py` to switch between different implementations.
-
-Results
+How to run
 -------
 
-Run with following (available dataset: "cora", "citeseer", "pubmed")
+### DGL built-in GraphConv module
+
+Run with the following (available dataset: "cora", "citeseer", "pubmed")
 ```bash
-python3 train.py --dataset cora --gpu 0 --self-loop
+python3 train.py --dataset cora
 ```
 
-* cora: ~0.810 (0.79-0.83) (paper: 0.815)
-* citeseer: 0.707 (paper: 0.703)
-* pubmed: 0.792 (paper: 0.790)
+Summary
+-------
+* cora: ~0.810 (paper: 0.815)
+* citeseer: ~0.707 (paper: 0.703)
+* pubmed: ~0.792 (paper: 0.790)
+