diff --git a/pyrival/graphs/bcc.py b/pyrival/graphs/edge_bcc.py
similarity index 93%
rename from pyrival/graphs/bcc.py
rename to pyrival/graphs/edge_bcc.py
index 01e1eda..307b29b 100644
--- a/pyrival/graphs/bcc.py
+++ b/pyrival/graphs/edge_bcc.py
@@ -33,7 +33,8 @@ def find_SCC(graph):
 
 """
 Given an undirected simple graph, find_BCC returns a list of lists 
-containing the biconnected components of the graph. Runs in O(n + m) time.
+containing the edge biconnected components of the graph (i.e. no bridges).
+Runs in O(n + m) time.
 """
 def find_BCC(graph):
     d = 0
diff --git a/pyrival/graphs/node_bcc.py b/pyrival/graphs/node_bcc.py
new file mode 100644
index 0000000..bc43c60
--- /dev/null
+++ b/pyrival/graphs/node_bcc.py
@@ -0,0 +1,62 @@
+"""
+Given an undirected graph,
+ 
+  1   4   7   10
+ / \ / \ / \ /
+2   0   5   9
+ \ / \ / \ /
+  3   6   8
+ 
+the cut_tree function returns the cut-block tree made out of 2 sets of nodes, the original
+nodes U in the graph and new nodes V corresponding to each node biconnected component. 
+There is an edge between (u,v) for u in U and v in V if u lies in bicomponent v. 
+In the case of an unconnected graph, the function returns a forest of block-cut trees.
+For the graph above, this is the tree returned by cut_tree
+ 
+    1        4        7        10
+    |        |        |        |
+2--(14)--0--(13)--5--(12)--9--(11)
+    |        |        | 
+    3        6        8
+ 
+Here the nodes in () denotes bicomponent nodes.
+"""
+def cut_tree(graph):
+    n = len(graph)
+    new_graph = [[] for _ in range(n)]
+ 
+    P = [-1] * n
+    depth = [-1] * n
+    biconnect = [None] * n
+    root = 0
+ 
+    preorder = []
+    stack = [root]
+    while stack:
+        node = stack.pop()
+        if depth[node] >= 0:
+            continue
+        depth[node] = len(preorder)
+        preorder.append(node)
+        for nei in graph[node]:
+            if depth[nei] == -1:
+                P[nei] = node
+                stack.append(nei)
+    preorder.pop(0)
+ 
+    for node in reversed(preorder):
+        depth[node] = min(depth[nei] for nei in graph[node]) 
+        if depth[P[node]] == depth[node]:
+            bicon = biconnect[node] = [P[node], node]
+            new_graph.append(bicon)
+   
+    for node in preorder:
+        if biconnect[node] is None:
+            bicon = biconnect[node] = biconnect[P[node]]
+            bicon.append(node)
+    
+    for bicon_ind in range(n, len(new_graph)):
+        for node in new_graph[bicon_ind]:
+            new_graph[node].append(bicon_ind)
+ 
+    return new_graph