Datasets:

Modalities:
Text
Formats:
text
ArXiv:
Tags:
art
Libraries:
Datasets
License:
SauravMaheshkar commited on
Commit
f7ac0ee
1 Parent(s): 7d66875

feat: add link generation script

Browse files
Files changed (1) hide show
  1. link_gen.py +105 -0
link_gen.py ADDED
@@ -0,0 +1,105 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import dgl
2
+ import torch
3
+ import pickle
4
+ from copy import deepcopy
5
+ import scipy.sparse as sp
6
+ import numpy as np
7
+
8
+
9
+ def mask_test_edges(adj_orig, val_frac, test_frac):
10
+
11
+ # Remove diagonal elements
12
+ adj = deepcopy(adj_orig)
13
+ # set diag as all zero
14
+ adj.setdiag(0)
15
+ adj.eliminate_zeros()
16
+ # Check that diag is zero:
17
+ # assert np.diag(adj.todense()).sum() == 0
18
+
19
+ adj_triu = sp.triu(adj, 1)
20
+ edges = sparse_to_tuple(adj_triu)[0]
21
+ num_test = int(np.floor(edges.shape[0] * test_frac))
22
+ num_val = int(np.floor(edges.shape[0] * val_frac))
23
+
24
+ all_edge_idx = list(range(edges.shape[0]))
25
+ np.random.shuffle(all_edge_idx)
26
+ val_edge_idx = all_edge_idx[:num_val]
27
+ test_edge_idx = all_edge_idx[num_val : (num_val + num_test)]
28
+ test_edges = edges[test_edge_idx]
29
+ val_edges = edges[val_edge_idx]
30
+ train_edges = edges[all_edge_idx[num_val + num_test :]]
31
+
32
+ noedge_mask = np.ones(adj.shape) - adj
33
+ noedges = np.asarray(sp.triu(noedge_mask, 1).nonzero()).T
34
+ all_edge_idx = list(range(noedges.shape[0]))
35
+ np.random.shuffle(all_edge_idx)
36
+ val_edge_idx = all_edge_idx[:num_val]
37
+ test_edge_idx = all_edge_idx[num_val : (num_val + num_test)]
38
+ test_edges_false = noedges[test_edge_idx]
39
+ val_edges_false = noedges[val_edge_idx]
40
+
41
+ data = np.ones(train_edges.shape[0])
42
+ adj_train = sp.csr_matrix(
43
+ (data, (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape
44
+ )
45
+ adj_train = adj_train + adj_train.T
46
+
47
+ train_mask = np.ones(adj_train.shape)
48
+ for edges_tmp in [val_edges, val_edges_false, test_edges, test_edges_false]:
49
+ for e in edges_tmp:
50
+ assert e[0] < e[1]
51
+ train_mask[edges_tmp.T[0], edges_tmp.T[1]] = 0
52
+ train_mask[edges_tmp.T[1], edges_tmp.T[0]] = 0
53
+
54
+ train_edges = np.asarray(sp.triu(adj_train, 1).nonzero()).T
55
+ train_edges_false = np.asarray(
56
+ (sp.triu(train_mask, 1) - sp.triu(adj_train, 1)).nonzero()
57
+ ).T
58
+
59
+ # NOTE: all these edge lists only contain single direction of edge!
60
+ return (
61
+ train_edges,
62
+ train_edges_false,
63
+ val_edges,
64
+ val_edges_false,
65
+ test_edges,
66
+ test_edges_false,
67
+ )
68
+
69
+
70
+ def sparse_to_tuple(sparse_mx):
71
+ if not sp.isspmatrix_coo(sparse_mx):
72
+ sparse_mx = sparse_mx.tocoo()
73
+ coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose()
74
+ values = sparse_mx.data
75
+ shape = sparse_mx.shape
76
+ return coords, values, shape
77
+
78
+
79
+ if __name__ == "__main__":
80
+ g, _ = dgl.load_graphs("./processed/chameleon.bin")
81
+ g = g[0]
82
+ total_pos_edges = torch.randperm(g.num_edges())
83
+ adj_train = g.adjacency_matrix(scipy_fmt="csr")
84
+ (
85
+ train_edges,
86
+ train_edges_false,
87
+ val_edges,
88
+ val_edges_false,
89
+ test_edges,
90
+ test_edges_false,
91
+ ) = mask_test_edges(adj_train, 0.1, 0.2)
92
+ tvt_edges_file = "./links/chameleon_tvtEdges.pkl"
93
+ pickle.dump(
94
+ (
95
+ train_edges,
96
+ train_edges_false,
97
+ val_edges,
98
+ val_edges_false,
99
+ test_edges,
100
+ test_edges_false,
101
+ ),
102
+ open(tvt_edges_file, "wb"),
103
+ )
104
+ node_assignment = dgl.metis_partition_assignment(g, 10)
105
+ torch.save(node_assignment, "./pretrain_labels/metis_label_chameleon.pt")