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
import networkx as nx
from node2vec import Node2Vec
# Create a graph
graph = 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 nodes
model = node2vec.fit(window=10, min_count=1, batch_words=4) # Any keywords acceptable by gensim.Word2Vec can be passed, `diemnsions` and `workers` are automatically passed (from the Node2Vec constructor)
# Look for most similar nodes
model.wv.most_similar('2') # Output node names are always strings
# Save embeddings for later use
model.wv.save_word2vec_format(EMBEDDING_FILENAME)
# Save model for later use
model.save(EMBEDDING_MODEL_FILENAME)
# Embed edges using Hadamard method
from node2vec.edges import HadamardEmbedder
edges_embs = HadamardEmbedder(keyed_vectors=model.wv)
# Look for embeddings on the fly - here we pass normal tuples
edges_embs[('1', '2')]
''' OUTPUT
array([ 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 networks
edges_kv = edges_embs.as_keyed_vectors()
# Look for most similar edges - this time tuples must be sorted and as str
edges_kv.most_similar(str(('1', '2')))
# Save embeddings for later use
edges_kv.save_word2vec_format(EDGES_EMBEDDING_FILENAME)
node2vec.Node2vecNode2Vec constructor:
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.dimensions: Embedding dimensions (default: 128)walk_length: Number of nodes in each walk (default: 80)num_walks: Number of walks per node (default: 10)p: Return hyper parameter (default: 1)q: Inout parameter (default: 1)weight_key: On weighted graphs, this is the key for the weight attribute (default: 'weight')workers: Number of workers for parallel execution (default: 1)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`quiet: Boolean controlling the verbosity. (default: False)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.Node2Vec.fit method:
Accepts any key word argument acceptable by gensim.Word2Vec
node2vec.EdgeEmbedderEdgeEmbedder is an abstract class which all the concrete edge embeddings class inherit from.
The classes are AverageEmbedder, HadamardEmbedder, WeightedL1Embedder and WeightedL2Embedder which their practical definition could be found in the paper on table 1
Notice that edge embeddings are defined for any pair of nodes, connected or not and even node with itself.
Constructor:
keyed_vectors: A gensim.models.KeyedVectors instance containing the node embeddingsquiet: Boolean controlling the verbosity. (default: False)EdgeEmbedder.__getitem__(item) method, better known as EdgeEmbedder[item]:
item - A tuple consisting of 2 nodes from the keyed_vectors passed in the constructor. Will return the embedding of the edge.EdgeEmbedder.as_keyed_vectors method: Returns a gensim.models.KeyedVectors instance with all possible node pairs in a sorted 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')".
joblib known issue). To run non-parallel on Windows pass workers=1 on the Node2Vec's constructor