# GraphEmbedding **Repository Path**: littlely_ll/GraphEmbedding ## Basic Information - **Project Name**: GraphEmbedding - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-01-05 - **Last Updated**: 2021-01-05 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 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
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# 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 ` ![](./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 ```