Function node2vec

easygraph/functions/graph_embedding/node2vec.py:13–118

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

(
    G,
    dimensions=128,
    walk_length=80,
    num_walks=10,
    p=1.0,
    q=1.0,
    weight_key=None,
    workers=None,
    **skip_gram_params,
)

@not_implemented_for("multigraph")
def node2vec(
    G,
    dimensions=128,
    walk_length=80,
    num_walks=10,
    p=1.0,
    q=1.0,
    weight_key=None,
    workers=None,
    **skip_gram_params,
):
    """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 [1]_
    ...          q=1.0, # The `q` possibility in random walk in [1]_