<commit_before># GEM: Graph Embedding Methods
GEM is a Python module that implements many graph (a.k.a. network) embedding algorithms. GEM is distributed under BSD license.

The module was developed and is maintained by <NAME>. The details of the methods implemented in this module are given in [Graph Embedding Techniques, Applications, and Performance: A Survey](https://arxiv.org/abs/1705.02801).

<commit_msg>Added list of methods in readme<commit_after># GEM: Graph Embedding Methods
GEM is a Python module that implements many graph (a.k.a. network) embedding algorithms. GEM is distributed under BSD license.

The module was developed and is maintained by <NAME>.

## Implemented Methods
GEM implements the following graph embedding techniques:
* [Laplacian Eigenmaps](http://yeolab.weebly.com/uploads/2/5/5/0/25509700/belkin_laplacian_2003.pdf)
* [Locally Linear Embedding](http://www.robots.ox.ac.uk/~az/lectures/ml/lle.pdf)
* [Graph Factorization](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/40839.pdf)
* [Higher-Prder Proximity preserved Embedding (HOPE)](http://www.kdd.org/kdd2016/papers/files/rfp0184-ouA.pdf)
* [Structural Deep Network Embedding (SDNE)](http://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf)
* [node2vec](http://www.kdd.org/kdd2016/papers/files/rfp0218-groverA.pdf)
A survey of these methods can be found in [Graph Embedding Techniques, Applications, and Performance: A Survey](https://arxiv.org/abs/1705.02801).

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