easygraph.functions.graph_embedding package

Submodules

easygraph.functions.graph_embedding.NOBE module

easygraph.functions.graph_embedding.NOBE.NOBE(G, K)[source]

Graph embedding via NOBE[1].

Parameters

  • G (easygraph.Graph) – An unweighted and undirected graph.

  • K (int) – Embedding dimension k

Returns

Y – list of embedding vectors (y1, y2, · · · , yn)

Return type

list

Examples

>>> NOBE(G,K=15)

References

1

https://www.researchgate.net/publication/325004496_On_Spectral_Graph_Embedding_A_Non-Backtracking_Perspective_and_Graph_Approximation

easygraph.functions.graph_embedding.NOBE.NOBE_GA(G, K)[source]

Graph embedding via NOBE-GA[1].

Parameters

  • G (easygraph.Graph) – An unweighted and undirected graph.

  • K (int) – Embedding dimension k

Returns

Y – list of embedding vectors (y1, y2, · · · , yn)

Return type

list

Examples

>>> NOBE_GA(G,K=15)

References

1

https://www.researchgate.net/publication/325004496_On_Spectral_Graph_Embedding_A_Non-Backtracking_Perspective_and_Graph_Approximation

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

1

https://arxiv.org/abs/1403.6652

easygraph.functions.graph_embedding.line module

easygraph.functions.graph_embedding.net_emb_example_citeseer module

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 [1]_
...          q=1.0, # The `q` possibility in random walk in [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

1

https://arxiv.org/abs/1607.00653

easygraph.functions.graph_embedding.sdne module

Module contents