import networkx as nx
from nodevectors import Node2Vec
# Test Graph
G = nx.generators.classic.wheel_graph(100)
# Fit embedding model to graph
g2v = Node2Vec()
# way faster than other node2vec implementations
# Graph edge weights are handled automatically
g2v.fit(G)
# query embeddings for node 42
g2v.predict(42)
# Save model to gensim.KeyedVector format
g2v.save("wheel_model.bin")
# load in gensim
from gensim.models import KeyedVectors
model = KeyedVectors.load_word2vec_format("wheel_model.bin")
model[str(43)] # need to make nodeID a str for gensim
pip install nodevectors
The public methods are all exposed in the quick example. The documentation is included in the docstrings of the methods, so for instance typing g2v.fit? in a Jupyter Notebook will expose the documentation directly.
We transform the graph into a CSR sparse matrix, and generate the random walks directly on the CSR matrix raw data with optimized Numba JIT'ed code. After that, a Word2Vec model is trained on the random walks, as if the walks were the Word2Vec sentences.