-
Notifications
You must be signed in to change notification settings - Fork 0
/
parser.py
119 lines (91 loc) · 4.05 KB
/
parser.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
import argparse
def parameter_parser():
parser = argparse.ArgumentParser(description="Run GraLSP.")
parser.add_argument("--dataset_name",
nargs="?",
default="cora",
help="The dataset to use, corresponds to a folder under data/")
parser.add_argument("--path_length",
type=int,
default=10,
help="The length of random_walks")
parser.add_argument("--num_paths",
type=int,
default=100,
help="The number of paths to use per node")
parser.add_argument("--window_size",
type=int,
default=6,
help="The window size to sample neighborhood")
parser.add_argument("--batch_size",
type=int,
default=100,
help="batch size")
parser.add_argument("--neg_size",
type=int,
default=8,
help="neg_size")
parser.add_argument("--learning_rate",
type=float,
default=0.002,
help="learning rate")
parser.add_argument("--embedding_dims",
type=int,
default=32,
help="The size of each embedding")
parser.add_argument("--epochs",
type=int,
default=1,
help="Steps to train")
parser.add_argument("--num_skips",
type=int,
default=5,
help="how many samples to draw from a single walk")
parser.add_argument("--num_neighbor",
type=int,
default=20,
help="How many neighbors to sample, for graphsage")
parser.add_argument("--hidden_dim",
type=int,
default=100,
help="The size of hidden dimension, for graphsage")
parser.add_argument("--walk_dim",
type=int,
default=30,
help="The size of embeddings for anonym. walks.")
parser.add_argument("--anonym_walk_len",
type=int,
default=8,
help="The length of each anonymous walk, 4 or 5")
parser.add_argument("--walk_loss_lambda",
type=float,
default=0.1,
help="Weight of loss focusing on anonym walk similarity")
parser.add_argument("--linkpred_ratio",
type=float,
default=0.1,
help="The ratio of edges being removed for link prediction")
parser.add_argument("--p",
type=float,
default=0.25,
help="return parameter for node2vec walk")
parser.add_argument("--q",
type=float,
default=1,
help="out parameter for node2vec walk")
parser.add_argument("--inductive",
type=int,
default=0,
help="whether to do inductive inference")
parser.add_argument("--inductive_model_epoch",
type=int,
default=None,
help="the epoch of the saved model")
parser.add_argument("--inductive_model_name",
type=str,
default=None,
help="the path towards the loaded model")
parser.add_argument("--save_path",
type = str,
default="embeddings/GraLSP")
return parser.parse_args()