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diff --git a/models/nullmodels/model_MDM.py b/models/nullmodels/model_MDM.py
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+++ b/models/nullmodels/model_MDM.py
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+####
+##
+##
+## This is the vertical participation model. For each node i, we use
+## exactly the same value of B_i as in the original network, but we
+## choose at random the layers in which node i will be active. This
+## breaks down intra-layer correlations. 
+##
+## We get as input a file which reports, for each value of B_i, the
+## number of nodes in the original network which have that value, i the format:
+##
+## B_i N(B_i)
+##
+##
+##
+## The output is the obtained distribution of bit-strings.
+##
+##
+
+import sys
+import random
+
+
+def to_binary(l):
+    s = 0
+    e = 0
+    for v in l:
+        s += v * pow(2,e)
+        e +=1
+    return s
+
+
+if len(sys.argv) < 3:
+    print "Usage: %s <Bi_file> <M>" % sys.argv[0]
+    sys.exit(1)
+
+M = int(sys.argv[2])
+
+layers = range(M)
+
+distr = {}
+
+with open(sys.argv[1], "r") as f:
+    for l in f:
+        if l[0] == "#":
+            continue
+        val, num = [int(x) for x in l.strip(" \n").split(" ")]
+        for j in range(num):
+            node_layers = random.sample(layers, val)
+            node_bitstring = [0 for x in range(M)]
+            #print node_bitstring, node_layers
+            for i in node_layers:
+                #print i,
+                node_bitstring[i] = 1
+                #print node_bitstring
+                
+            bs = to_binary(node_bitstring)
+            if bs in distr:
+                distr[bs] += 1
+            else:
+                distr[bs] = 1
+
+for k in distr:
+    bin_list = bin(k)
+    bin_num = sum([int(x) if x=='1' else 0 for x in bin_list[2:]])
+    sys.stderr.write("%d %0175s %d \n" % (bin_num, bin_list[2:], distr[k]))