| 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 |
- clone the repo and make sure you have installed
tensorflowortensorflow-gpuon your local machine. - run following commands
python setup.py install
cd examples
python deepwalk_wiki.py
公众号：浅梦学习笔记
|
微信：deepctrbot
|
The design and implementation follows simple principles(graph in,embedding out) as much as possible.
we use networkxto create graphs.The input of networkx graph is as follows:
node1 node2 <edge_weight>
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 vectorsG = 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 vectorsG=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 vectorsG = 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 vectorsG = 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
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