easygraph.functions.graph_embedding.deepwalk module#

easygraph.functions.graph_embedding.deepwalk.deepwalk(G, dimensions=128, walk_length=80, num_walks=10, **skip_gram_params)[source]#

Graph embedding via DeepWalk.

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)

  • 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

>>> deepwalk(G,
...          dimensions=128, # The graph embedding dimensions.
...          walk_length=80, # Walk length of each random walks.
...          num_walks=10, # Number of random walks.
...          skip_gram_params = dict( # The skip_gram parameters in Python package gensim.
...          window=10,
...             min_count=1,
...             batch_words=4,
...             iter=15
...          ))

References