/node2vec

Implementation of the node2vec algorithm.

Primary LanguagePythonMIT LicenseMIT

Node2Vec

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.

Installation

pip install node2vec

Usage

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
node2vec = Node2Vec(graph, dimensions=64, walk_length=30, num_walks=200, workers=4) 

# Embed
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)

Parameters

  • Node2Vec constructor:

    1. 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.
    2. dimensions: Embedding dimensions (default: 128)
    3. walk_length: Number of nodes in each walk (default: 80)
    4. num_walks: Number of walks per node (default: 10)
    5. p: Return hyper parameter (default: 1)
    6. q: Inout parameter (default: 1)
    7. weight_key: On weighted graphs, this is the key for the weight attribute (default: 'weight')
    8. workers: Number of workers for parallel execution (default: 1)
    9. 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`

  • Node2Vec.fit method: Accepts any key word argument acceptable by gensim.Word2Vec

Caveats

  • Node names in the input graph must be all strings, or all ints
  • Does not work on Anaconda + Windows

TODO

  • Parallel implementation for walk generation
  • Parallel implementation for probability precomputation

Contributing

I will probably not be maintaining this package actively, if someone wants to contribute and maintain, please contact me.