-
Notifications
You must be signed in to change notification settings - Fork 1
/
main.py
187 lines (165 loc) · 4.75 KB
/
main.py
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
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
# -*- coding: utf-8 -*-
"""
Personalized Link Prediction
Created on Fri Oct 21 09:33:16 2016
@author: Longjie Li
"""
# import math
import lp
#import test
'''----------------------------------------------------------------------------
参数设置
'''
t = 50 # 独立实验的次数
p = 0.1 # 测试数据的比例，测试集的大小
# alpha = 0.01 # 部分算法中的参数
# beta = 0.01 # 部分算法中的参数
suf = str(p * 10) +'-mm'
path = '.\\Networks\\' # 网络数据的根目录
# 网络文件
networks = [
'CE\\celegansneural.edgelist', # 0
'jazz\\jazz.edgelist', # 1
'karate\\karate.edgelist', # 2
'netscience\\netscience.edgelist', # 3
'USAir\\USAir97.edgelist', # 4
'power\\power.edgelist', # 5 12197676
'yeast\\yeast.edgelist', # 6 2465367
'PGP\\PGP.edgelist', # 7 57001544
'Dolphins\\dolphins.edgelist', # 8
'PB\\polblogs.edgelist', # 9
'email\\email.edgelist', # 10
'Hamster\\Hamster.edgelist', # 11 1585102
'HEP\\hep.edgelist', # 12 17006880
'word\\word.edgelist', # 13
'foodweb\\baywet.edgelist', # 14
'Router\\Router.edgelist', # 15 12601473
'INF\\INF.edgelist', # 16
'FBK\\FaceBook.edgelist', # 17 7970223
'ADV\\ADV.edgelist', # 18 12669134
'Wikivote\\Wikivote.edgelist', # 19 25207293
'SciMet\\SciMet.edgelist', # 20
'Florida\\Florida.edgelist', # 21
'Kohonen\\Kohonen.net', # 22
'Mangdry\\mangdry.edgelist', # 23
'Mangwet\\mangwet.edgelist', # 24
'Lederberg\\Lederberg.edgelist', # 25
'GrQc\\GrQc.edgelist', # 26
'Metabolic\\metabolic.edgelist', # 27
'OpenFlights\\openflights.edgelist',# 28
'SmallGri\\SmallGri.edgelist', # 29
'test\\test.edgelist' #
]
# 对应的结果文件
results = [
'.\\results\\CE-',
'.\\results\\Jazz-',
'.\\results\\Karate-',
'.\\results\\NS-',
'.\\results\\USAir-',
'.\\results\\Power-',
'.\\results\\Yeast-',
'.\\results\\PGP-',
'.\\results\\Dolphins-',
'.\\results\\PB-',
'.\\results\\Email-',
'.\\results\\Hamster-',
'.\\results\\HEP-',
'.\\results\\Word-',
'.\\results\\FoodWeb-',
'.\\results\\Router-',
'.\\results\\INF-',
'.\\results\\FaceBook-',
'.\\results\\ADV-',
'.\\results\\Wikivote',
'.\\results\\SciMet',
'.\\results\\Florida',
'.\\results\\Kohonen',
'.\\results\\Mangdry',
'.\\results\\Mangwet',
'.\\results\\Lederberg',
'.\\results\\GrQc-',
'.\\results\\Metabolic-',
'.\\results\\Flight-',
'.\\results\\SmallGri-',
'.\\results\\test-',
]
# 实验中可能只使用部分网络， 下面数组中指定相应网络的id
net_ids = [2]#, 7, 8, 15, 17, 18]#
#net_ids = [26,27,28,29]
#net_ids = [6, 11]
graph_file_list = [] # 网络文件列表
result_file_list = [] # 结果文件列表
for i in net_ids:
graph_file_list.append(path + networks[i])
result_file_list.append(results[i] + suf)
# end for
# 相似性方法
sim_methods = [
'CN', # 0
'AA', # 1
'RA', # 2
'Jaccard', # 3
'PA', # 4
'CN_PA', # 5
'ADP', # 6
'CNaD', # 7
'ERA', # 8
'LP', # 9
'HCR', # 10
'CAR', # 11
'CRA', # 12
'CAA', # 13
'CPA', # 14
'CJC', # 15
'LCCL', # 16
'CCLP', # 17
'NLC', # 18
'LNB_CN', # 19
'LNB_AA', # 20
'LNB_RA', # 21
'MI', # 22
'PNR_CN', # 23
'PNR_AA', # 24
'PNR_RA', # 25
'PNR_JC', # 26
'MAX', # 27
'TOP', # 28
'ED_CN', # 29
'ED_AA', # 30
'ED_RA', # 31
'MI', # 32
'CMI', # 33
'CMI2', # 34
'MM', # 35
'MA', # 36
'MA2', # 37
'MI5', # 38
'CLNB_CN', # 39
'CLNB2_CN', # 40
'CLNB2_AA', # 41
'CLNB2_RA', # 42
'LPP',#43
'L',#44
]
# 实验中使用的方法的id
method_ids = [0]
sim_method_list = [sim_methods[i] for i in method_ids]
# 按照数据集，分别计算
for i in range(len(graph_file_list)):
graph_file = graph_file_list[i]
result_file = result_file_list[i]
out_file = open(result_file, 'w') # 打开结果文件
print(graph_file)
# 输出标题
out_file.write('Method\tAUC\tRanking_Score\ttime (ms)\tPrecision (10)\n')
# 按照不同的相似度方法分别计算
for method in sim_method_list:
print(method)
out_file.write(method + '\t')
lp.LP(graph_file, out_file, method, t, p)
out_file.flush()
# end for
out_file.close()
# end for
###############################################################################