add gcnii

examples/citation_benchmark/README.md

 # Easy Paper Reproduction for Citation Network (Cora/Pubmed/Citeseer)
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+This page tries to reproduce all the **Graph Neural Network** paper for Citation Network (Cora/Pubmed/Citeseer), which is the **Hello world**  dataset (**small** and **fast**) for graph neural networks. But it's very hard to achieve very high performance.
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+All datasets are runned with public split of  **semi-supervised** settings.
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+How to run the experiments?
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+```shell
+# Device choose
+export CUDA_VISIBLE_DEVICES=0
+# GCN
+python train.py --conf config/gcn.yaml --use_cuda --dataset cora
+python train.py --conf config/gcn.yaml --use_cuda --dataset pubmed
+python train.py --conf config/gcn.yaml --use_cuda --dataset citeseer
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+
+# GAT
+python train.py --conf config/gat.yaml --use_cuda --dataset cora
+python train.py --conf config/gat.yaml --use_cuda --dataset pubmed
+python train.py --conf config/gat.yaml --use_cuda --dataset citeseer
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+
+# SGC (Slow version)
+python train.py --conf config/sgc.yaml --use_cuda --dataset cora
+python train.py --conf config/sgc.yaml --use_cuda --dataset pubmed
+python train.py --conf config/sgc.yaml --use_cuda --dataset citeseer
+
+# APPNP
+python train.py --conf config/appnp.yaml --use_cuda --dataset cora
+python train.py --conf config/appnp.yaml --use_cuda --dataset pubmed
+python train.py --conf config/appnp.yaml --use_cuda --dataset citeseer
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+# GCNII (The original code use 1500 epochs.)
+python train.py --conf config/appnp.yaml --use_cuda --dataset cora --epoch 1500
+python train.py --conf config/appnp.yaml --use_cuda --dataset pubmed --epoch 1500
+python train.py --conf config/appnp.yaml --use_cuda --dataset citeseer --epoch 1500
+```
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