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135 lines
3.4 KiB
135 lines
3.4 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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## layer-by-layer multiplex growth
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##
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## - We start from a multiplex with M_0 layers, with a certain number of
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## active nodes each
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##
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## - Each new layer arrives with a certain number N\lay{\alpha} of nodes
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## to be activated (this number is sampled from the observed distribution
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## of N\lay{\alpha}, like in the airlines multiplex)
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##
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## - Each node $i$ is activated with a probability proportional to the
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## number of existing layers in which it is already active, added to an
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## attractivity A :
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##
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## P_i(t) \propto A + B_i(t)
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##
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## - the hope is that A might tune the exponent of the resulting distribution
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## of B_i.....
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##
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##
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## This script takes as input a file which contains the degrees of the
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## layers, the total number of nodes in the multiplex, the initial
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## number M0 of layers in the multiplex and the attractiveness
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## parameter A. If "RND" is specified as a third parameter, then the
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## sequence of N\lay{\alpha} is shuffled
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##
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## Gives as output a list of node-layer participation
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##
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import sys
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import random
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if len(sys.argv) < 5:
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print "Usage: %s <layers_N> <N> <M0> <A> [RND]" % sys.argv[0]
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sys.exit(1)
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N = int(sys.argv[2])
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M0 = int(sys.argv[3])
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A = int(sys.argv[4])
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layer_degs = []
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if len(sys.argv) > 5 and sys.argv[5] == "RND":
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randomize = True
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else:
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randomize = False
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lines = open(sys.argv[1]).readlines()
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M = 0
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for l in lines:
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if l[0] == "#":
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continue
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n = [int(x) for x in l.strip(" \n").split(" ")][0]
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layer_degs.append(n)
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M += 1
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if randomize:
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random.shuffle(layer_degs)
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## the list node_Bi contains, at each time, the attachment
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## probabilities, i.e. it is a list which contains each node $i$
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## exactly $A + B_i$ times
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node_Bi = []
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#
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# initialize the distribution of attachment proibabilities, giving to
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# all nodes an attachment probability equal to A
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#
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for i in range(N):
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for j in range(A):
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node_Bi.append(i)
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layers = []
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for i in range(M0):
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N_alpha = layer_degs.pop()
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node_list = []
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num_nodes = 0
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while num_nodes < N_alpha:
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val = random.choice(range(N))
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if val not in node_list:
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node_list.append(val)
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num_nodes += 1
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for n in node_list:
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node_Bi.append(n)
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layers.append(node_list)
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i += 1
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#sys.exit(1)
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while i < M:
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N_alpha = layer_degs.pop()
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node_list = []
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num_nodes = 0
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while num_nodes < N_alpha:
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val = random.choice(node_Bi)
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if val not in node_list:
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node_list.append(val)
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num_nodes += 1
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for n in node_list:
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node_Bi.append(n)
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layers.append(node_list)
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i += 1
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#print layers
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for i in range(M):
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node_list = layers[i]
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for n in node_list:
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print n, i
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