update README.

README.md

@@ -36,40 +36,40 @@ In detail, the following methods are currently implemented:
 ## Semi-supervised models
 ### General 
 
-+ **ChebyNet** from *Michaël Defferrard et al*, [📝Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://arxiv.org/abs/1606.09375), *NIPS'16*
++ **ChebyNet** from *Michaël Defferrard et al*, [📝Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://arxiv.org/abs/1606.09375), *NIPS'16*. 
  [[:octocat:Official Codes]](https://github.com/mdeff/cnn_graph), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_ChebyNet.ipynb)
-+ **GCN** from *Thomas N. Kipf et al*, [📝Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907), *ICLR'17*
++ **GCN** from *Thomas N. Kipf et al*, [📝Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907), *ICLR'17*. 
  [[:octocat:Official Codes]](https://github.com/tkipf/gcn), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_GCN.ipynb)
-+ **GraphSAGE** from *William L. Hamilton et al*, [📝Inductive Representation Learning on Large Graphs](https://arxiv.org/abs/1706.02216), *NIPS'17*
++ **GraphSAGE** from *William L. Hamilton et al*, [📝Inductive Representation Learning on Large Graphs](https://arxiv.org/abs/1706.02216), *NIPS'17*. 
  [[:octocat:Official Codes]](https://github.com/williamleif/GraphSAGE), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_GraphSAGE.ipynb)
-+ **FastGCN** from *Jie Chen et al*, [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://arxiv.org/abs/1801.10247), *ICLR'18*
++ **FastGCN** from *Jie Chen et al*, [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://arxiv.org/abs/1801.10247), *ICLR'18*. 
 [[:octocat:Official Codes]](https://github.com/matenure/FastGCN), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_FastGCN.ipynb)
-+ **LGCN** from  *Hongyang Gao et al*, [📝Large-Scale Learnable Graph Convolutional Networks](https://arxiv.org/abs/1808.03965), *KDD'18*
++ **LGCN** from  *Hongyang Gao et al*, [📝Large-Scale Learnable Graph Convolutional Networks](https://arxiv.org/abs/1808.03965), *KDD'18*. 
  [[:octocat:Official Codes]](https://github.com/divelab/lgcn), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_LGCN.ipynb)
-+ **GAT** from *Petar Veličković et al*, [📝Graph Attention Networks](https://arxiv.org/abs/1710.10903), *ICLR'18* 
++ **GAT** from *Petar Veličković et al*, [📝Graph Attention Networks](https://arxiv.org/abs/1710.10903), *ICLR'18*.  
 [[:octocat:Official Codes]](https://github.com/PetarV-/GAT), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_GAT.ipynb)
-+ **SGC** from *Felix Wu et al*, [📝Simplifying Graph Convolutional Networks](https://arxiv.org/abs/1902.07153), *ICML'19*
++ **SGC** from *Felix Wu et al*, [📝Simplifying Graph Convolutional Networks](https://arxiv.org/abs/1902.07153), *ICML'19*. 
  [[:octocat:Official Codes]](https://github.com/Tiiiger/SGC), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_SGC.ipynb)
-+ **GWNN** from *Bingbing Xu et al*, [📝Graph Wavelet Neural Network](https://arxiv.org/abs/1904.07785), *ICLR'19
-, [[:octocat:Official Codes]](https://github.com/Eilene/GWNN), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_GWNN.ipynb)
-+ **GMNN** from *Meng Qu et al*, [📝Graph Markov Neural Networks](https://arxiv.org/abs/1905.06214), *ICML'19*
++ **GWNN** from *Bingbing Xu et al*, [📝Graph Wavelet Neural Network](https://arxiv.org/abs/1904.07785), *ICLR'19*. 
+[[:octocat:Official Codes]](https://github.com/Eilene/GWNN), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_GWNN.ipynb)
++ **GMNN** from *Meng Qu et al*, [📝Graph Markov Neural Networks](https://arxiv.org/abs/1905.06214), *ICML'19*. 
  [[:octocat:Official Codes]](https://github.com/DeepGraphLearning/GMNN), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_GMNN.ipynb)
-+ **ClusterGCN** from *Wei-Lin Chiang et al*, [📝Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://arxiv.org/abs/1905.07953), *KDD'19*
++ **ClusterGCN** from *Wei-Lin Chiang et al*, [📝Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://arxiv.org/abs/1905.07953), *KDD'19*. 
  [[:octocat:Official Codes]](https://github.com/google-research/google-research/tree/master/cluster_gcn), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_ClusterGCN.ipynb)
-+ **DAGNN** from *Meng Liu et al*, [📝Towards Deeper Graph Neural Networks](https://arxiv.org/abs/2007.09296), *KDD'20*
++ **DAGNN** from *Meng Liu et al*, [📝Towards Deeper Graph Neural Networks](https://arxiv.org/abs/2007.09296), *KDD'20*. 
  [[:octocat:Official Codes]](https://github.com/mengliu1998/DeeperGNN), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_DAGNN.ipynb)
 
 
 ### Defense models
-+ **RobustGCN** from *Dingyuan Zhu et al*, [📝Robust Graph Convolutional Networks Against Adversarial Attacks](https://dl.acm.org/doi/10.1145/3292500.3330851), *KDD'19
-, [[:octocat:Official Codes]](https://github.com/thumanlab/nrlweb/blob/master/static/assets/download/RGCN.zip), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_RobustGCN.ipynb)
-+ **SBVAT/OBVAT** from *Zhijie Deng et al*, [📝Batch Virtual Adversarial Training for Graph Convolutional Networks](https://arxiv.org/abs/1902.09192), *ICML'19
-, [[:octocat:Official Codes]](https://github.com/thudzj/BVAT)
++ **RobustGCN** from *Dingyuan Zhu et al*, [📝Robust Graph Convolutional Networks Against Adversarial Attacks](https://dl.acm.org/doi/10.1145/3292500.3330851), *KDD'19*. 
+[[:octocat:Official Codes]](https://github.com/thumanlab/nrlweb/blob/master/static/assets/download/RGCN.zip), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_RobustGCN.ipynb)
++ **SBVAT/OBVAT** from *Zhijie Deng et al*, [📝Batch Virtual Adversarial Training for Graph Convolutional Networks](https://arxiv.org/abs/1902.09192), *ICML'19*. 
+[[:octocat:Official Codes]](https://github.com/thudzj/BVAT)
 
 ## Unsupervised models
-+ **Deepwalk** from *Bryan Perozzi et al*, [📝DeepWalk: Online Learning of Social Representations](https://arxiv.org/abs/1403.6652), *KDD'14*
++ **Deepwalk** from *Bryan Perozzi et al*, [📝DeepWalk: Online Learning of Social Representations](https://arxiv.org/abs/1403.6652), *KDD'14*. 
  [[:octocat:Official Codes]](https://github.com/phanein/deepwalk), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_Deepwalk.ipynb)
-+ **Node2vec** from *Aditya Grover et al*, [📝node2vec: Scalable Feature Learning for Networks](https://arxiv.org/abs/1607.00653), *KDD'16*
++ **Node2vec** from *Aditya Grover et al*, [📝node2vec: Scalable Feature Learning for Networks](https://arxiv.org/abs/1607.00653), *KDD'16*. 
  [[:octocat:Official Codes]](https://github.com/aditya-grover/node2vec), [[🌈 GraphGallery Example]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/test_Node2vec.ipynb)
 
 # Quick Start
@@ -179,6 +179,12 @@ with plt.style.context(['science', 'no-latex']):
 # More Examples
 Please refer to the [examples](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples) directory.
 
+# TODO Lists
+- [ ] Add Docstrings and Documentation
+- [ ] Support for `graph Classification` and `link prediction` tasks
+- [ ] Support for Heterogeneous graphs
+- [ ] Add PyTorch models support
+
 # Acknowledgement
 This project is motivated by [Pytorch Geometric](https://github.com/rusty1s/pytorch_geometric), [Tensorflow Geometric](https://github.com/CrawlScript/tf_geometric) and [Stellargraph](https://github.com/stellargraph/stellargraph), and the original implementations from the authors, thanks for their excellent works!