Code: add GCN-VAE

Author: kinkunchan

.DS_Store

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+The MIT License
+
+Copyright (c) 2017 Thomas Kipf
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

Tutorial.ipynb

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+Graph Auto-Encoders
+============
+
+This is a TensorFlow implementation of the (Variational) Graph Auto-Encoder model as described in our paper:
+ 
+T. N. Kipf, M. Welling, [Variational Graph Auto-Encoders](https://arxiv.org/abs/1611.07308), NIPS Workshop on Bayesian Deep Learning (2016)
+
+Graph Auto-Encoders (GAEs) are end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs. 
+
+![(Variational) Graph Auto-Encoder](figure.png)
+
+GAEs have successfully been used for:
+* Link prediction in large-scale relational data: M. Schlichtkrull & T. N. Kipf et al., [Modeling Relational Data with Graph Convolutional Networks](https://arxiv.org/abs/1703.06103) (2017),
+* Matrix completion / recommendation with side information: R. Berg et al., [Graph Convolutional Matrix Completion](https://arxiv.org/abs/1706.02263) (2017).
+
+
+GAEs are based on Graph Convolutional Networks (GCNs), a recent class of models for end-to-end (semi-)supervised learning on graphs:
+
+T. N. Kipf, M. Welling, [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907), ICLR (2017). 
+
+A high-level introduction is given in our blog post:
+
+Thomas Kipf, [Graph Convolutional Networks](http://tkipf.github.io/graph-convolutional-networks/) (2016)
+
+
+
+## Installation
+
+```bash
+python setup.py install
+```
+
+## Requirements
+* TensorFlow (1.0 or later)
+* python 2.7
+* networkx
+* scikit-learn
+* scipy
+
+## Run the demo
+
+```bash
+python train.py
+```
+
+## Data
+
+In order to use your own data, you have to provide 
+* an N by N adjacency matrix (N is the number of nodes), and
+* an N by D feature matrix (D is the number of features per node) -- optional
+
+Have a look at the `load_data()` function in `input_data.py` for an example.
+
+In this example, we load citation network data (Cora, Citeseer or Pubmed). The original datasets can be found here: http://linqs.cs.umd.edu/projects/projects/lbc/ and here (in a different format): https://github.com/kimiyoung/planetoid
+
+You can specify a dataset as follows:
+
+```bash
+python train.py --dataset citeseer
+```
+
+(or by editing `train.py`)
+
+## Models
+
+You can choose between the following models: 
+* `gcn_ae`: Graph Auto-Encoder (with GCN encoder)
+* `gcn_vae`: Variational Graph Auto-Encoder (with GCN encoder)
+
+## Cite
+
+Please cite our paper if you use this code in your own work:
+
+```
+@article{kipf2016variational,
+  title={Variational Graph Auto-Encoders},
+  author={Kipf, Thomas N and Welling, Max},
+  journal={NIPS Workshop on Bayesian Deep Learning},
+  year={2016}
+}
+```

semb/methods/gae/LICENCE

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+from __future__ import print_function
+from __future__ import division