Awesome

GraphEmbedding

Method

Model	Paper	Note
DeepWalk	[KDD 2014]DeepWalk: Online Learning of Social Representations	【Graph Embedding】DeepWalk：算法原理，实现和应用
LINE	[WWW 2015]LINE: Large-scale Information Network Embedding	【Graph Embedding】LINE：算法原理，实现和应用
Node2Vec	[KDD 2016]node2vec: Scalable Feature Learning for Networks	【Graph Embedding】Node2Vec：算法原理，实现和应用
SDNE	[KDD 2016]Structural Deep Network Embedding	【Graph Embedding】SDNE：算法原理，实现和应用
Struc2Vec	[KDD 2017]struc2vec: Learning Node Representations from Structural Identity	【Graph Embedding】Struc2Vec：算法原理，实现和应用

How to run examples

clone the repo and make sure you have installed tensorflow or tensorflow-gpu on your local machine.
run following commands

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 networkxto create graphs.The input of networkx graph is as follows: node1 node2 <edge_weight>

DeepWalk

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

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

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

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

G = nx.read_edgelist('../data/flight/brazil-airports.edgelist',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph

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