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112 lines
2.7 KiB
112 lines
2.7 KiB
# This file is part of MAMMULT: Metrics And Models for Multilayer Networks
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or (at
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# your option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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# General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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####
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##
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##
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## Compute the degree-degree correlations of a multiplex graph, namely:
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##
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## <k_1>(k_2) and <k_2>(k_1)
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##
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## Takes as input the two lists of edges corresponding to each layer
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##
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import sys
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import numpy as np
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import networkx as net
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def knn(G, n):
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neigh = G.neighbors(n)
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l = G.degree(neigh).values()
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return 1.0 * sum(l) / len(l)
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if len(sys.argv) < 2:
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print "Usage: %s <layer1> <layer2>" % sys.argv[0]
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sys.exit(1)
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G1 = net.read_edgelist(sys.argv[1])
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G2 = net.read_edgelist(sys.argv[2])
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k1_k1 = {} ## Intraleyer knn (k1)
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k2_k2 = {} ## Intralayer knn (k2)
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k1_k2 = {} ## Interlayer average degree at layer 1 of a node having degree k_2 in layer 2
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k2_k1 = {} ## Interlayer average degree at layer 2 of a node having degree k_1 in layer 1
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for n in G1.nodes():
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k1 = G1.degree(n)
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##print k1,k2
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knn1 = knn(G1, n)
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if n in G2.nodes():
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k2 = G2.degree(n)
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knn2 = knn(G2, n)
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else:
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k2 = 0
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knn2 = 0
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if k1_k1.has_key(k1):
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k1_k1[k1].append(knn1)
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else:
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k1_k1[k1] = [knn1]
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if k2_k2.has_key(k2):
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k2_k2[k2].append(knn2)
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else:
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k2_k2[k2] = [knn2]
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if k1_k2.has_key(k2):
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k1_k2[k2].append(k1)
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else:
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k1_k2[k2] = [k1]
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if k2_k1.has_key(k1):
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k2_k1[k1].append(k2)
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else:
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k2_k1[k1] = [k2]
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k1_keys = k1_k1.keys()
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k1_keys.sort()
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k2_keys = k2_k2.keys()
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k2_keys.sort()
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f = open("%s_%s_k1" % (sys.argv[1], sys.argv[2]), "w+")
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for n in k1_keys:
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avg_knn = np.mean(k1_k1[n])
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std_knn = np.std(k1_k1[n])
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avg_k2 = np.mean(k2_k1[n])
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std_k2 = np.std(k2_k1[n])
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f.write("%d %f %f %f %f\n" % (n, avg_knn, std_knn, avg_k2, std_k2))
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f.close()
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f = open("%s_%s_k2" % (sys.argv[1], sys.argv[2]), "w+")
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for n in k2_keys:
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avg_knn = np.mean(k2_k2[n])
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std_knn = np.std(k2_k2[n])
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avg_k1 = np.mean(k1_k2[n])
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std_k1 = np.std(k1_k2[n])
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f.write("%d %f %f %f %f\n" % (n, avg_knn, std_knn, avg_k1, std_k1))
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f.close()
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