# GraphEmbedding
# Method
| Model | Paper | Note |
| :-------: | :------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------ |
| DeepWalk | [KDD 2014][DeepWalk: Online Learning of Social Representations](http://www.perozzi.net/publications/14_kdd_deepwalk.pdf) | [【Graph Embedding】DeepWalk：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56380812) |
| LINE | [WWW 2015][LINE: Large-scale Information Network Embedding](https://arxiv.org/pdf/1503.03578.pdf) | [【Graph Embedding】LINE：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56478167) |
| Node2Vec | [KDD 2016][node2vec: Scalable Feature Learning for Networks](https://www.kdd.org/kdd2016/papers/files/rfp0218-groverA.pdf) | [【Graph Embedding】Node2Vec：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56542707) |
| SDNE | [KDD 2016][Structural Deep Network Embedding](https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf) | [【Graph Embedding】SDNE：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56637181) |
| Struc2Vec | [KDD 2017][struc2vec: Learning Node Representations from Structural Identity](https://arxiv.org/pdf/1704.03165.pdf) | [【Graph Embedding】Struc2Vec：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56733145) |
# How to run examples
1. clone the repo and make sure you have installed `tensorflow` or `tensorflow-gpu` on your local machine.
2. run following commands
```bash
python setup.py install
cd examples
python deepwalk_wiki.py
```
## DisscussionGroup & Related Projects
<html>
<table style="margin-left: 20px; margin-right: auto;">
<tr>
<td>
公众号：<b>浅梦的学习笔记</b><br><br>
<a href="https://github.com/shenweichen/GraphEmbedding">
<img align="center" src="./pics/code.png" />
</a>
</td>
<td>
微信：<b>deepctrbot</b><br><br>
<a href="https://github.com/shenweichen/GraphEmbedding">
<img align="center" src="./pics/deepctrbot.png" />
</a>
</td>
<td>
<ul>
<li><a href="https://github.com/shenweichen/AlgoNotes">AlgoNotes</a></li>
<li><a href="https://github.com/shenweichen/DeepCTR">DeepCTR</a></li>
<li><a href="https://github.com/shenweichen/DeepMatch">DeepMatch</a></li>
<li><a href="https://github.com/shenweichen/DeepCTR-Torch">DeepCTR-Torch</a></li>
</ul>
</td>
</tr>
</table>
</html>
# Usage
The design and implementation follows simple principles(**graph in,embedding out**) as much as possible.
## Input format
we use `networkx`to create graphs.The input of networkx graph is as follows:
`node1 node2 <edge_weight>`
## DeepWalk
```python
G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])# Read graph
model = DeepWalk(G,walk_length=10,num_walks=80,workers=1)#init model
model.train(window_size=5,iter=3)# train model
embeddings = model.get_embeddings()# get embedding vectors
```
## LINE
```python
G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
model = LINE(G,embedding_size=128,order='second') #init model,order can be ['first','second','all']
model.train(batch_size=1024,epochs=50,verbose=2)# train model
embeddings = model.get_embeddings()# get embedding vectors
```
## Node2Vec
```python
G=nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',
create_using = nx.DiGraph(), nodetype = None, data = [('weight', int)])#read graph
model = Node2Vec(G, walk_length = 10, num_walks = 80,p = 0.25, q = 4, workers = 1)#init model
model.train(window_size = 5, iter = 3)# train model
embeddings = model.get_embeddings()# get embedding vectors
```
## SDNE
```python
G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
model = SDNE(G,hidden_size=[256,128]) #init model
model.train(batch_size=3000,epochs=40,verbose=2)# train model
embeddings = model.get_embeddings()# get embedding vectors
```
## Struc2Vec
```python
G = nx.read_edgelist('../data/flight/brazil-airports.edgelist',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
model = model = Struc2Vec(G, 10, 80, workers=4, verbose=40, ) #init model
model.train(window_size = 5, iter = 3)# train model
embeddings = model.get_embeddings()# get embedding vectors
```