blob: 46b4c0f467304b9a84aefa9701bb0ba801e6031b (
plain)
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
|
####
##
## 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
|
