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authorKatolaZ <katolaz@yahoo.it>2015-10-19 16:23:00 +0100
committerKatolaZ <katolaz@yahoo.it>2015-10-19 16:23:00 +0100
commitdf8386f75b0538075d72d52693836bb8878f505b (patch)
tree704c2a0836f8b9fd9f470c12b6ae05637c431468 /structure/correlations/knn_q_from_layers.py
parent363274e79eade464247089c105260bc34940da07 (diff)
First commit of MAMMULT code
Diffstat (limited to 'structure/correlations/knn_q_from_layers.py')
-rw-r--r--structure/correlations/knn_q_from_layers.py98
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
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+++ b/structure/correlations/knn_q_from_layers.py
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+####
+##
+##
+## 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()
+