diff --git a/requirements.txt b/requirements.txt
index faa710c..52067c4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,3 @@
-networkx==1.11
-numpy==1.11.2
-gensim==0.13.3
+numpy>=1.16.2
+networkx>=2.2
+gensim>=3.4.0
diff --git a/src/main.py b/src/main.py
index 82ac735..53f12c8 100644
--- a/src/main.py
+++ b/src/main.py
@@ -83,9 +83,9 @@ 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
 
diff --git a/src/node2vec.py b/src/node2vec.py
index 0293411..59d821e 100644
--- a/src/node2vec.py
+++ b/src/node2vec.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 import numpy as np
 import networkx as nx
 import random
@@ -43,9 +44,9 @@ 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))