blob: 2c30df52b9df54db36f40554ec98d59dad055abe (
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
|
####
##
##
## 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]
|
