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diff --git a/models/nullmodels/model_layer_growth.py b/models/nullmodels/model_layer_growth.py
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+####
+##
+## layer-by-layer multiplex growth
+##
+## - We start from a multiplex with M_0 layers, with a certain number of 
+##   active nodes each
+##
+## - Each new layer arrives with a certain number N\lay{\alpha} of nodes
+##   to be activated (this number is sampled from the observed distribution 
+##   of N\lay{\alpha}, like in the airlines multiplex)
+## 
+## - Each node $i$ is activated with a probability proportional to the
+##   number of existing layers in which it is already active, added to an
+##   attractivity A :
+##
+## P_i(t) \propto A + B_i(t)
+##
+## - the hope is that A might tune the exponent of the resulting distribution
+##   of B_i.....
+## 
+##
+## This script takes as input a file which contains the degrees of the
+## layers, the total number of nodes in the multiplex, the initial
+## number M0 of layers in the multiplex and the attractiveness
+## parameter A. If "RND" is specified as a third parameter, then the
+## sequence of N\lay{\alpha} is shuffled
+##
+## Gives as output a list of node-layer participation
+##
+
+import sys
+import random
+
+if len(sys.argv) < 5:
+    print "Usage: %s <layers_N> <N> <M0> <A> [RND]" % sys.argv[0]
+    sys.exit(1)
+
+N = int(sys.argv[2])
+M0 = int(sys.argv[3])
+A = int(sys.argv[4])
+
+layer_degs = []
+
+
+if len(sys.argv) > 5 and sys.argv[5] == "RND":
+    randomize = True
+else:
+    randomize = False
+
+lines = open(sys.argv[1]).readlines()
+
+M = 0
+
+
+for l in lines:
+    if l[0] == "#":
+        continue
+    n = [int(x) for x in l.strip(" \n").split(" ")][0]
+    layer_degs.append(n)
+    M += 1
+
+
+if randomize:
+    random.shuffle(layer_degs)
+
+## the list node_Bi contains, at each time, the attachment
+## probabilities, i.e. it is a list which contains each node $i$
+## exactly $A + B_i$ times
+
+node_Bi = []
+
+#
+# initialize the distribution of attachment proibabilities, giving to
+# all nodes an attachment probability equal to A
+#
+
+for i in range(N):
+    for j in range(A):
+        node_Bi.append(i)
+        
+layers = []
+
+
+for i in range(M0):
+    N_alpha = layer_degs.pop()
+    node_list = []
+    num_nodes = 0
+    while num_nodes < N_alpha:
+        val = random.choice(range(N))
+        if val not in node_list:
+            node_list.append(val)
+            num_nodes += 1
+    for n in node_list:
+        node_Bi.append(n)
+    layers.append(node_list)
+    i += 1
+
+
+#sys.exit(1)
+
+while i < M:
+    N_alpha = layer_degs.pop()
+    node_list = []
+    num_nodes = 0
+    while num_nodes < N_alpha:
+        val = random.choice(node_Bi)
+        if val not in node_list:
+            node_list.append(val)
+            num_nodes += 1
+    for n in node_list:
+        node_Bi.append(n)
+    layers.append(node_list)
+    i += 1
+
+#print layers
+
+for i in range(M):
+    node_list = layers[i]
+    for n in node_list:
+        print n, i
+