diff --git a/src/absinth.py b/src/absinth.py
index 1c527ff87cdfeac6d2be72ce36bad9eb822cfe9c..e777ccf267d9c3cc4f9a9ff1e155d5ef8dd10b75 100644
--- a/src/absinth.py
+++ b/src/absinth.py
@@ -151,7 +151,7 @@ def induce(topic_name: str, result_list: list) -> (nx.Graph, list, dict):
                 edge_freq_dict = {(key1,key2):value2 for key1,value1 in edge_freq_dict.items() for key2,value2 in value1.items()}
 
                 print('[a] Collected frequencies from secret hideout.')
-                
+
             continue
     
     if graph_in_existence == False:
@@ -1022,8 +1022,12 @@ def main(topic_id: int, topic_name: str, result_dict: dict) -> None:
         
         edge_count = len(graph.edges)
         node_count = len(graph.nodes)
-        mean_degree = edge_count/node_count
-        
+
+        try:
+            mean_degree = edge_count/node_count
+        except ZeroDivisionError:
+            mean_degree = 0
+
         stat_dict['L_rand'] = np.log(node_count)/np.log(mean_degree)
         stat_dict['C_rand'] = 2 * mean_degree/node_count
         
diff --git a/src/abstinent.py b/src/abstinent.py
index e4aa17079f02e8f019e6b668d14352738ceaec3e..5536301951ed8adecfba1b54111bafb0286291a4 100644
--- a/src/abstinent.py
+++ b/src/abstinent.py
@@ -368,7 +368,7 @@ def induce(topic_name: str, result_list: list) -> (nx.Graph, list, dict):
 
                 edge_freq_dict = {(key1,key2):value2 for key1,value1 in edge_freq_dict.items() for key2,value2 in value1.items()}
 
-                print('[a] Collected frequencies from hidden hideout.')
+                print('[a] Collected frequencies from secret hideout.')
 
             continue
     
@@ -634,8 +634,12 @@ def main(topic_id: int, topic_name: str, result_dict: dict) -> None:
         
         edge_count = len(graph.edges)
         node_count = len(graph.nodes)
-        mean_degree = edge_count/node_count
-        
+
+        try:
+            mean_degree = edge_count/node_count
+        except ZeroDivisionError:
+            mean_degree = 0
+
         stat_dict['L_rand'] = np.log(node_count)/np.log(mean_degree)
         stat_dict['C_rand'] = 2 * mean_degree/node_count