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Minimal Python 2/3 compatibility. #31

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Minimal Python 2/3 compatibility. #31

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10 changes: 5 additions & 5 deletions src/main.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,11 @@
'''
Reference implementation of node2vec.
Reference implementation of node2vec.

Author: Aditya Grover

For more details, refer to the paper:
node2vec: Scalable Feature Learning for Networks
Aditya Grover and Jure Leskovec
Aditya Grover and Jure Leskovec
Knowledge Discovery and Data Mining (KDD), 2016
'''

Expand Down Expand Up @@ -83,10 +83,10 @@ def learn_embeddings(walks):
'''
Learn embeddings by optimizing the Skipgram objective using SGD.
'''
walks = [map(str, walk) for walk in walks]
walks = [list(map(str, walk)) for walk in walks]
model = Word2Vec(walks, size=args.dimensions, window=args.window_size, min_count=0, sg=1, workers=args.workers, iter=args.iter)
model.save_word2vec_format(args.output)
model.wv.save_word2vec_format(args.output)

return

def main(args):
Expand Down
10 changes: 6 additions & 4 deletions src/node2vec.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
from __future__ import print_function

import numpy as np
import networkx as nx
import random
Expand Down Expand Up @@ -28,7 +30,7 @@ def node2vec_walk(self, walk_length, start_node):
walk.append(cur_nbrs[alias_draw(alias_nodes[cur][0], alias_nodes[cur][1])])
else:
prev = walk[-2]
next = cur_nbrs[alias_draw(alias_edges[(prev, cur)][0],
next = cur_nbrs[alias_draw(alias_edges[(prev, cur)][0],
alias_edges[(prev, cur)][1])]
walk.append(next)
else:
Expand All @@ -43,9 +45,9 @@ def simulate_walks(self, num_walks, walk_length):
G = self.G
walks = []
nodes = list(G.nodes())
print 'Walk iteration:'
print('Walk iteration:')
for walk_iter in range(num_walks):
print str(walk_iter+1), '/', str(num_walks)
print(str(walk_iter+1), '/', str(num_walks))
random.shuffle(nodes)
for node in nodes:
walks.append(self.node2vec_walk(walk_length=walk_length, start_node=node))
Expand Down Expand Up @@ -146,4 +148,4 @@ def alias_draw(J, q):
if np.random.rand() < q[kk]:
return kk
else:
return J[kk]
return J[kk]