easygraph.functions.graph_embedding.node2vec module#

easygraph.functions.graph_embedding.node2vec.node2vec(G, dimensions=128, walk_length=80, num_walks=10, p=1.0, q=1.0, weight_key=None, workers=None, **skip_gram_params)[source]#

Graph embedding via Node2Vec.

Parameters:

  • G (easygraph.Graph or easygraph.DiGraph) –

  • dimensions (int) – Embedding dimensions, optional(default: 128)

  • walk_length (int) – Number of nodes in each walk, optional(default: 80)

  • num_walks (int) – Number of walks per node, optional(default: 10)

  • p (float) – The return hyper parameter, optional(default: 1.0)

  • q (float) – The input parameter, optional(default: 1.0)

  • weight_key (string or None (default: None)) – On weighted graphs, this is the key for the weight attribute

  • workers (int or None, optional(default : None)) – The number of workers generating random walks (default: None). None if not using only one worker.

  • skip_gram_params (dict) – Parameters for gensim.models.Word2Vec - do not supply ‘size’, it is taken from the ‘dimensions’ parameter

Returns:

  • embedding_vector (dict) – The embedding vector of each node

  • most_similar_nodes_of_node (dict) – The most similar nodes of each node and its similarity

Examples

>>> node2vec(G,
...          dimensions=128, # The graph embedding dimensions.
...          walk_length=80, # Walk length of each random walks.
...          num_walks=10, # Number of random walks.
...          p=1.0, # The `p` possibility in random walk in [Rcb3fa857cc7d-1]_
...          q=1.0, # The `q` possibility in random walk in [Rcb3fa857cc7d-1]_
...          weight_key='weight',
...          skip_gram_params=dict( # The skip_gram parameters in Python package gensim.
...          window=10,
...             min_count=1,
...             batch_words=4
...          ))

References