First commit of MAMMULT code

1 files changed, 98 insertions, 0 deletions

diff --git a/structure/correlations/knn_q_from_layers.py b/structure/correlations/knn_q_from_layers.py
new file mode 100644
index 0000000..c108c18
--- /dev/null
+++ b/structure/correlations/knn_q_from_layers.py
@@ -0,0 +1,98 @@
+####
+##
+##
+## Compute the degree-degree correlations of a multiplex graph, namely:
+##
+## <k_1>(k_2) and <k_2>(k_1)
+##
+## Takes as input the two lists of edges corresponding to each layer
+##
+
+import sys
+import numpy as np
+import networkx as net
+
+
+def knn(G, n):
+    neigh = G.neighbors(n)
+    l = G.degree(neigh).values()
+    return 1.0 * sum(l) / len(l) 
+
+
+if len(sys.argv) < 2:
+    print "Usage: %s <layer1> <layer2>" % sys.argv[0]
+    sys.exit(1)
+
+
+G1 = net.read_edgelist(sys.argv[1])
+G2 = net.read_edgelist(sys.argv[2])
+
+k1_k1 = {} ## Intraleyer knn (k1)
+k2_k2 = {} ## Intralayer knn (k2)
+k1_k2 = {} ## Interlayer average degree at layer 1 of a node having degree k_2 in layer 2
+k2_k1 = {} ## Interlayer average degree at layer 2 of a node having degree k_1 in layer 1
+
+
+for n in G1.nodes():
+    k1 = G1.degree(n)
+
+    
+    ##print k1,k2
+
+    knn1 = knn(G1, n)
+    if n in G2.nodes():
+        k2 = G2.degree(n)
+        knn2 = knn(G2, n)
+    else:
+        k2 = 0
+        knn2 = 0
+
+    if k1_k1.has_key(k1):
+        k1_k1[k1].append(knn1)
+    else:
+        k1_k1[k1] = [knn1]
+
+    if k2_k2.has_key(k2):
+        k2_k2[k2].append(knn2)
+    else:
+        k2_k2[k2] = [knn2]
+        
+
+    if k1_k2.has_key(k2):
+        k1_k2[k2].append(k1)
+    else:
+        k1_k2[k2] = [k1]
+
+    if k2_k1.has_key(k1):
+        k2_k1[k1].append(k2)
+    else:
+        k2_k1[k1] = [k2]
+        
+        
+k1_keys = k1_k1.keys()
+k1_keys.sort()
+k2_keys = k2_k2.keys()
+k2_keys.sort()
+
+
+f = open("%s_%s_k1" % (sys.argv[1], sys.argv[2]), "w+")
+
+for n in k1_keys:
+    avg_knn = np.mean(k1_k1[n])
+    std_knn = np.std(k1_k1[n])
+    avg_k2 = np.mean(k2_k1[n])
+    std_k2 = np.std(k2_k1[n])
+    f.write("%d %f %f %f %f\n" % (n, avg_knn, std_knn, avg_k2, std_k2))
+
+f.close()
+
+f = open("%s_%s_k2" % (sys.argv[1], sys.argv[2]), "w+")
+
+for n in k2_keys:
+    avg_knn = np.mean(k2_k2[n])
+    std_knn = np.std(k2_k2[n])
+    avg_k1 = np.mean(k1_k2[n])
+    std_k1 = np.std(k1_k2[n])
+    f.write("%d %f %f %f %f\n" % (n, avg_knn, std_knn, avg_k1, std_k1))
+f.close()
+