requirements and ge module

AKSW · May 31, 2022 · 6d6fa18 · 6d6fa18
1 parent b26148b
commit 6d6fa18
Show file tree

Hide file tree

Showing 36 changed files with 47,326 additions and 0 deletions.
diff --git a/GraphEmbeddings/LICENSE b/GraphEmbeddings/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Weichen Shen
+
+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.
diff --git a/GraphEmbeddings/README.md b/GraphEmbeddings/README.md
@@ -0,0 +1,107 @@
+# 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>`
+
+![](./pics/edge_list.png)
+## 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
+```