updated to latest version of libraries

requirements.txt

@@ -1,3 +1,3 @@
-networkx==1.11
-numpy==1.11.2
-gensim==0.13.3
+networkx==2.8
+numpy>=1.22.3
+gensim==4.2.0

src/main.py

@@ -84,8 +84,13 @@ def learn_embeddings(walks):
 	Learn embeddings by optimizing the Skipgram objective using SGD.
 	'''
 	walks = [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)
+	# size=args.dimensions
+	model = Word2Vec(walks,
+					 vector_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.save(args.output)
 
 	return
 

src/node2vec.py

@@ -43,9 +43,9 @@ 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))