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Sparse Node2Vec Wrapper for Large Networks
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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
# script created using: https://github.com/VHRanger/nodevectors | |
# import needed libraries | |
import argparse | |
import csrgraph as cg | |
import nodevectors | |
def main(): | |
parser = argparse.ArgumentParser(description='A wrapper for running Node2Vec on Very Large Graphs') | |
parser.add_argument('-e', '--edgelist', help='Name/path to text file containing graph edge list', required=True) | |
parser.add_argument('-d', '--dim', help='Embedding dimensions', required=True) | |
parser.add_argument('-l', '--walklen', help='Random walk length', required=True) | |
parser.add_argument('-r', '--walknum', help='Number of walks', required=True) | |
parser.add_argument('-t', '--threads', help='# threads to use', default=0) | |
parser.add_argument('-p', '--return_weight', help='Return node probability', default=1.) | |
parser.add_argument('-q', '--explore_weight', help='Node visit probability', default=1.) | |
parser.add_argument('-k', '--window', help='Context window size', required=True) | |
parser.add_argument('-w', '--keep_walks', help='Save the random walks', default=False) | |
parser.add_argument('-m', '--save_model', help='Save Gensim node2vec model', default=False) | |
args = parser.parse_args() | |
# print user parameters to console | |
print('\n#######################################################################\n') | |
print('NODE2VEC Parameters:') | |
print('Edge List: {input_file}'.format(input_file=args.edgelist.split('/')[-1])) | |
print('Embedding Dimensions: {dim}'.format(dim=args.dim)) | |
print('Random walk Length: {walk_len}'.format(walk_len=args.walklen)) | |
print('Number of random walks: {walk_num}'.format(walk_num=args.walknum)) | |
print('Threads: {threads}'.format(threads=args.threads)) | |
print('Return Weight (p): {p}'.format(p=args.return_weight)) | |
print('Explore Weight (q): {q}'.format(q=args.explore_weight)) | |
print('Context Window Size: {window_size}'.format(window_size=args.window)) | |
print('Save Random Walks with Node2Vec Model: {keep_walks}'.format(keep_walks=args.keep_walks)) | |
print('Save Gensim Node2Vec Model: {save_model}'.format(save_model=args.save_model)) | |
print('Embedding output: {write_loc}'.format(write_loc=args.edgelist.split('.')[0] + '_node2vec_Embeddings.emb')) | |
print('\n#######################################################################\n') | |
print('\n#### STEP 1: Convert Edge List to CSR Graph ####') | |
graph = cg.read_edgelist(args.edgelist, sep=' ', header=None) | |
print('\n#### STEP 2: Fit Embedding Model to Graph ####') | |
g2v = nodevectors.Node2Vec(n_components=int(args.dim), | |
walklen=int(args.walklen), | |
epochs=int(args.walknum), | |
return_weight=float(args.return_weight), | |
neighbor_weight=float(args.explore_weight), | |
threads=int(args.threads), | |
keep_walks=args.keep_walks, | |
verbose=True, | |
w2vparams={'window': int(args.window), 'iter': 10}) | |
g2v.fit(graph) | |
print('\n#### STEP 3: Save Model Output and Embeddings ####') | |
# save embeddings (gensim.KeyedVector format) | |
g2v.save_vectors(args.edgelist.split('.')[0] + '_node2vec_Embeddings.emb') | |
if args.save_model: | |
# save node2vec model -- uses a lot of memory and takes a very long time to run on large graphs | |
g2v.save(args.edgelist.split('.')[0] + '_node2vec_Model.pkl') | |
# g2v = Node2vec.load(args.input.split('.')[0] + '_node2vec_Model.pkl') | |
if __name__ == '__main__': | |
main() |
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Complete Before Running node2vec
Install needed libraries
user:~$ pip install argparse csrgraph nodevectors tqdm
STEP 1
Map Identifiers-based
subject
,predicate
, andobject
entities to integers.STEP 2
Node2vec requires the graph to be formatted in a specific way (
nodea nodeb
) rather thannodea edge nodeb
like most programs output. The code chunk below reads in a text file containing edges lists formatted as triples and outputs a modified edge list with the edges removed (i.e.nodea nodeb
).