diff --git a/graphsage/minibatch.py b/graphsage/minibatch.py
index 180648d..60c7b97 100644
--- a/graphsage/minibatch.py
+++ b/graphsage/minibatch.py
@@ -42,15 +42,15 @@ class EdgeMinibatchIterator(object):
         self.train_edges = self.edges = np.random.permutation(edges)
         if not n2v_retrain:
             self.train_edges = self._remove_isolated(self.train_edges)
-            self.val_edges = [e for e in G.edges_iter() if G[e[0]][e[1]]['train_removed']]
+            self.val_edges = [e for e in G.edges() if G[e[0]][e[1]]['train_removed']]
         else:
             if fixed_n2v:
                 self.train_edges = self.val_edges = self._n2v_prune(self.edges)
             else:
                 self.train_edges = self.val_edges = self.edges
 
-        print(len([n for n in G.nodes_iter() if not G.node[n]['test'] and not G.node[n]['val']]), 'train nodes')
-        print(len([n for n in G.nodes_iter() if G.node[n]['test'] or G.node[n]['val']]), 'test nodes')
+        print(len([n for n in G.nodes() if not G.node[n]['test'] and not G.node[n]['val']]), 'train nodes')
+        print(len([n for n in G.nodes() if G.node[n]['test'] or G.node[n]['val']]), 'test nodes')
         self.val_set_size = len(self.val_edges)
 
     def _n2v_prune(self, edges):
@@ -150,7 +150,7 @@ class EdgeMinibatchIterator(object):
     def label_val(self):
         train_edges = []
         val_edges = []
-        for n1, n2 in self.G.edges_iter():
+        for n1, n2 in self.G.edges():
             if (self.G.node[n1]['val'] or self.G.node[n1]['test'] 
                     or self.G.node[n2]['val'] or self.G.node[n2]['test']):
                 val_edges.append((n1,n2))
@@ -197,8 +197,8 @@ class NodeMinibatchIterator(object):
         self.adj, self.deg = self.construct_adj()
         self.test_adj = self.construct_test_adj()
 
-        self.val_nodes = [n for n in self.G.nodes_iter() if self.G.node[n]['val']]
-        self.test_nodes = [n for n in self.G.nodes_iter() if self.G.node[n]['test']]
+        self.val_nodes = [n for n in self.G.nodes() if self.G.node[n]['val']]
+        self.test_nodes = [n for n in self.G.nodes() if self.G.node[n]['test']]
 
         self.no_train_nodes_set = set(self.val_nodes + self.test_nodes)
         self.train_nodes = set(G.nodes()).difference(self.no_train_nodes_set)
diff --git a/graphsage/supervised_train.py b/graphsage/supervised_train.py
index 9580149..240d9aa 100644
--- a/graphsage/supervised_train.py
+++ b/graphsage/supervised_train.py
@@ -125,8 +125,8 @@ def train(train_data, test_data=None):
     features = train_data[1]
     id_map = train_data[2]
     class_map  = train_data[4]
-    if isinstance(class_map.values()[0], list):
-        num_classes = len(class_map.values()[0])
+    if isinstance(list(class_map.values())[0], list):
+        num_classes = len(list(class_map.values())[0])
     else:
         num_classes = len(set(class_map.values()))
 
diff --git a/graphsage/utils.py b/graphsage/utils.py
index a8254d2..23c6b52 100644
--- a/graphsage/utils.py
+++ b/graphsage/utils.py
@@ -25,20 +25,20 @@ def load_data(prefix, normalize=True, load_walks=False):
         print("No features present.. Only identity features will be used.")
         feats = None
     id_map = json.load(open(prefix + "-id_map.json"))
-    id_map = {conversion(k):int(v) for k,v in id_map.iteritems()}
+    id_map = {conversion(k):int(v) for k,v in id_map.items()}
     walks = []
     class_map = json.load(open(prefix + "-class_map.json"))
-    if isinstance(class_map.values()[0], list):
+    if isinstance(list(class_map.values())[0], list):
         lab_conversion = lambda n : n
     else:
         lab_conversion = lambda n : int(n)
 
-    class_map = {conversion(k):lab_conversion(v) for k,v in class_map.iteritems()}
+    class_map = {conversion(k):lab_conversion(v) for k,v in class_map.items()}
 
     ## Make sure the graph has edge train_removed annotations
     ## (some datasets might already have this..)
     print("Loaded data.. now preprocessing..")
-    for edge in G.edges_iter():
+    for edge in G.edges():
         if (G.node[edge[0]]['val'] or G.node[edge[1]]['val'] or
             G.node[edge[0]]['test'] or G.node[edge[1]]['test']):
             G[edge[0]][edge[1]]['train_removed'] = True