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