1 files changed, 65 insertions, 0 deletions
diff --git a/models/nullmodels/model_MSM.py b/models/nullmodels/model_MSM.py
new file mode 100644
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+++ b/models/nullmodels/model_MSM.py
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+####
+##
+##
+## Create a synthetic multiplex network in which a node $i$ appears at
+## each layer $\alpha$ with a probability equal to $B_i$, which is the
+## fraction of layers in which node $i$ participate in the original
+## multiplex.
+##
+## Take a file of node binary participation indices, and sample a
+## multiplex compatible with that distribution
+##
+##
+## The input file is in the format:
+## 
+## nodeID_i B_i
+##
+## The output file is a node-layer participation file, in the format
+##
+## node_id1 layer_id1
+## node_id2 layer_id2
+## .....
+##
+
+import sys
+
+import random
+
+if len(sys.argv) < 3:
+    print "Usage: %s <filein> <M>" % sys.argv[0]
+    sys.exit(1)
+
+M = int(sys.argv[2])
+
+bin_index = {}
+node_ids = []
+
+lines = open(sys.argv[1]).readlines()
+
+
+for l in lines:
+    if l[0] == "#":
+        continue
+    elems = [int(x) for x in l.strip(" \n").split(" ")]
+    bin_index[elems[0]] = 1.0 * elems[1]/M
+    node_ids.append(elems[0])
+
+N = len(node_ids)
+
+node_layers = {}
+
+for alpha in range (M):
+    for i in node_ids:
+        val = random.random()
+        if val < bin_index[i]:
+            if  node_layers.has_key(i):
+                node_layers[i].append(alpha)
+            else:
+                node_layers[i] = [alpha]
+
+
+for i in node_ids:
+    if node_layers.has_key(i):
+        for j in range(len(node_layers[i])):
+            print i, node_layers[i][j]
+