[Model][Hetero] GCMC using new hetero APIs (#860)

* init

* rm data

* README

* fix rating values that are not decimal

* rm stale codes

* small fix

* upd

* rewrite decoder

* fix many

* many fix; performance matched

* upd; handle sparse input

* upd

* address comments

* more docstring; download data automatically

* shared param mode

examples/mxnet/gcmc/.gitignore

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+log

examples/mxnet/gcmc/README.md

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+# Graph Convolutional Matrix Completion
+
+Paper link: [https://arxiv.org/abs/1706.02263](https://arxiv.org/abs/1706.02263)
+Author's code: [https://github.com/riannevdberg/gc-mc](https://github.com/riannevdberg/gc-mc)
+
+The implementation does not handle side-channel features and mini-epoching and thus achieves
+slightly worse performance when using node features.
+
+Credit: Jiani Zhang ([@jennyzhang0215](https://github.com/jennyzhang0215))
+
+## Dependencies
+* MXNet 1.5.0+
+* pandas
+* gluonnlp
+
+## Data
+
+Supported datasets: ml-100k, ml-1m, ml-10m
+
+## How to run
+
+ml-100k, no feature
+```bash
+DGLBACKEND=mxnet python train.py --data_name=ml-100k --use_one_hot_fea --gcn_agg_accum=stack
+```
+Results: RMSE=0.9077 (0.910 reported)
+Speed: 0.0246s/epoch (vanilla implementation: 0.1008s/epoch)
+
+ml-100k, with feature
+```bash
+DGLBACKEND=mxnet python train.py --data_name=ml-100k --gcn_agg_accum=stack
+```
+Results: RMSE=0.9495 (0.905 reported)
+
+ml-1m, no feature
+```bash
+DGLBACKEND=mxnet python train.py --data_name=ml-1m --gcn_agg_accum=sum --use_one_hot_fea
+```
+Results: RMSE=0.8377 (0.832 reported)
+Speed: 0.0695s/epoch (vanilla implementation: 1.538s/epoch)
+
+ml-10m, no feature
+```bash
+DGLBACKEND=mxnet python train.py --data_name=ml-10m --gcn_agg_accum=stack --gcn_dropout=0.3 \
+                                 --train_lr=0.001 --train_min_lr=0.0001 --train_max_iter=15000 \
+                                 --use_one_hot_fea --gen_r_num_basis_func=4
+```
+Results: RMSE=0.7875 (0.777 reported)
+Speed: 0.6480s/epoch (vanilla implementation: OOM)
+
+Testbed: EC2 p3.2xlarge