1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
|
# 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/>.
####
##
## layer-by-layer multiplex growth
##
## - We start from a multiplex with M_0 layers, with a certain number of
## active nodes each
##
## - Each new layer arrives with a certain number N\lay{\alpha} of nodes
## to be activated (this number is sampled from the observed distribution
## of N\lay{\alpha}, like in the airlines multiplex)
##
## - Each node $i$ is activated with a probability proportional to the
## number of existing layers in which it is already active, added to an
## attractivity A :
##
## P_i(t) \propto A + B_i(t)
##
## - the hope is that A might tune the exponent of the resulting distribution
## of B_i.....
##
##
## This script takes as input a file which contains the degrees of the
## layers, the total number of nodes in the multiplex, the initial
## number M0 of layers in the multiplex and the attractiveness
## parameter A. If "RND" is specified as a third parameter, then the
## sequence of N\lay{\alpha} is shuffled
##
## Gives as output a list of node-layer participation
##
import sys
import random
if len(sys.argv) < 5:
print "Usage: %s <layers_N> <N> <M0> <A> [RND]" % sys.argv[0]
sys.exit(1)
N = int(sys.argv[2])
M0 = int(sys.argv[3])
A = int(sys.argv[4])
layer_degs = []
if len(sys.argv) > 5 and sys.argv[5] == "RND":
randomize = True
else:
randomize = False
lines = open(sys.argv[1]).readlines()
M = 0
for l in lines:
if l[0] == "#":
continue
n = [int(x) for x in l.strip(" \n").split(" ")][0]
layer_degs.append(n)
M += 1
if randomize:
random.shuffle(layer_degs)
## the list node_Bi contains, at each time, the attachment
## probabilities, i.e. it is a list which contains each node $i$
## exactly $A + B_i$ times
node_Bi = []
#
# initialize the distribution of attachment proibabilities, giving to
# all nodes an attachment probability equal to A
#
for i in range(N):
for j in range(A):
node_Bi.append(i)
layers = []
for i in range(M0):
N_alpha = layer_degs.pop()
node_list = []
num_nodes = 0
while num_nodes < N_alpha:
val = random.choice(range(N))
if val not in node_list:
node_list.append(val)
num_nodes += 1
for n in node_list:
node_Bi.append(n)
layers.append(node_list)
i += 1
#sys.exit(1)
while i < M:
N_alpha = layer_degs.pop()
node_list = []
num_nodes = 0
while num_nodes < N_alpha:
val = random.choice(node_Bi)
if val not in node_list:
node_list.append(val)
num_nodes += 1
for n in node_list:
node_Bi.append(n)
layers.append(node_list)
i += 1
#print layers
for i in range(M):
node_list = layers[i]
for n in node_list:
print n, i
|
