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# This file is part of MAMMULT: Metrics And Models for Multilayer Networks
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or (at
# your option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
####
##
##
## 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]
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