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Python3 implementation of the node2vec algorithm Aditya Grover, Jure Leskovec and Vid Kocijan.node2vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.

pip install node2vec

importnetworkxasnxfromnode2vecimportNode2Vec# Create a graphgraph=nx.fast_gnp_random_graph(n=100,p=0.5)# Precompute probabilities and generate walks - **ON WINDOWS ONLY WORKS WITH workers=1**node2vec=Node2Vec(graph,dimensions=64,walk_length=30,num_walks=200,workers=4)# Use temp_folder for big graphs# Embed nodesmodel=node2vec.fit(window=10,min_count=1,batch_words=4)# Any keywords acceptable by gensim.Word2Vec can be passed, `dimensions` and `workers` are automatically passed (from the Node2Vec constructor)# Look for most similar nodesmodel.wv.most_similar('2')# Output node names are always strings# Save embeddings for later usemodel.wv.save_word2vec_format(EMBEDDING_FILENAME)# Save model for later usemodel.save(EMBEDDING_MODEL_FILENAME)# Embed edges using Hadamard methodfromnode2vec.edgesimportHadamardEmbedderedges_embs=HadamardEmbedder(keyed_vectors=model.wv)# Look for embeddings on the fly - here we pass normal tuplesedges_embs[('1','2')]''' OUTPUTarray([ 5.75068220e-03, -1.10937878e-02,  3.76693785e-01,  2.69105062e-02,       ... ... ....       ..................................................................],      dtype=float32)'''# Get all edges in a separate KeyedVectors instance - use with caution could be huge for big networksedges_kv=edges_embs.as_keyed_vectors()# Look for most similar edges - this time tuples must be sorted and as stredges_kv.most_similar(str(('1','2')))# Save embeddings for later useedges_kv.save_word2vec_format(EDGES_EMBEDDING_FILENAME)

• Node2Vec constructor:

  1. graph: The first positional argument has to be a networkx graph. Node names must be all integers or all strings. On the output model they will always be strings.
  2. dimensions: Embedding dimensions (default: 128)
  3. walk_length: Number of nodes in each walk (default: 80)
  4. num_walks: Number of walks per node (default: 10)
  5. p: Return hyper parameter (default: 1)
  6. q: Input parameter (default: 1)
  7. weight_key: On weighted graphs, this is the key for the weight attribute (default: 'weight')
  8. workers: Number of workers for parallel execution (default: 1)
  9. sampling_strategy: Node specific sampling strategies, supports setting node specific 'q', 'p', 'num_walks' and 'walk_length'.Use these keys exactly. If not set, will use the global ones which were passed on the object initialization`
  10. quiet: Boolean controlling the verbosity. (default: False)
  11. temp_folder: String path pointing to folder to save a shared memory copy of the graph - Supply when working on graphs that are too big to fit in memory during algorithm execution.
  12. seed: Seed for the random number generator (default: None). Deterministic results can be obtained if seed is set andworkers=1.

• Node2Vec.fit method:Accepts any key word argument acceptable by gensim.Word2Vec

EdgeEmbedder is an abstract class which all the concrete edge embeddings class inherit from.The classes areAverageEmbedder,HadamardEmbedder,WeightedL1Embedder andWeightedL2Embedder which their practical definition could be found in thepaper on table 1Notice that edge embeddings are defined for any pair of nodes, connected or not and even node with itself.

• Constructor:

  1. keyed_vectors: A gensim.models.KeyedVectors instance containing the node embeddings
  2. quiet: Boolean controlling the verbosity. (default: False)

• EdgeEmbedder.__getitem__(item) method, better known asEdgeEmbedder[item]:

  1. item - A tuple consisting of 2 nodes from thekeyed_vectors passed in the constructor. Will return the embedding of the edge.

• EdgeEmbedder.as_keyed_vectors method: Returns agensim.models.KeyedVectors instance with all possible node pairs in asorted manner as string.For example, for nodes ['1', '2', '3'] we will have as keys "('1', '1')", "('1', '2')", "('1', '3')", "('2', '2')", "('2', '3')" and "('3', '3')".

• Node names in the input graph must be all strings, or all ints
• Parallel execution not working on Windows (joblib known issue). To run non-parallel on Windows passworkers=1 on theNode2Vec's constructor

• Parallel implementation for walk generation
• Parallel implementation for probability precomputation