hyperedge
int64
1
10.8k
nodes
stringclasses
883 values
timestamp
float64
0
81.3B
1
[1, 4]
36,541,320,000
2
[129, 117, 1]
38,032,620,000
3
[1, 51]
39,133,200,000
4
[1, 51]
39,313,440,000
5
[41, 1]
39,324,720,000
6
[1, 117]
39,660,360,000
7
[41, 1]
40,161,600,000
8
[65, 1]
41,113,920,000
9
[41, 1]
41,127,960,000
10
[1, 107]
41,995,500,000
11
[1, 107]
42,010,080,000
12
[129, 1]
42,165,420,000
13
[1, 122]
42,256,920,000
14
[1, 51]
42,686,340,000
15
[1, 51]
42,929,580,000
16
[41, 1]
44,064,660,000
17
[1, 122, 29]
44,416,920,000
18
[65, 1, 133, 122, 29, 62]
48,037,020,000
19
[1, 117]
48,387,780,000
20
[1, 4]
49,335,540,000
21
[65, 1]
49,848,540,000
22
[65, 1]
49,933,500,000
23
[1, 51]
51,069,000,000
24
[41, 51, 1]
51,165,180,000
25
[65, 1]
52,288,800,000
26
[65, 1, 41]
52,375,920,000
27
[1, 51]
52,525,020,000
28
[1, 71]
52,871,940,000
29
[1, 117]
53,044,320,000
30
[129, 117, 1]
53,153,520,000
31
[1, 29]
53,649,420,000
32
[65, 97, 1, 41, 51, 29]
53,650,140,000
33
[1, 63]
54,434,160,000
34
[1, 117]
54,534,660,000
35
[1, 133, 41, 51, 122, 29]
54,673,140,000
36
[120, 1]
54,693,360,000
37
[1, 51]
54,871,500,000
38
[1, 98]
55,384,200,000
39
[1, 98]
55,464,600,000
40
[1, 98]
55,470,480,000
41
[65, 97, 1, 41, 51, 29]
56,511,180,000
42
[1, 98]
56,771,220,000
43
[41, 29, 1]
56,854,860,000
44
[1, 63]
57,262,500,000
45
[97, 1, 41, 74, 51, 29]
57,281,940,000
46
[1, 63]
57,726,000,000
47
[1, 51]
57,732,600,000
48
[1, 71]
57,798,960,000
49
[1, 63]
58,503,540,000
50
[1, 23]
59,793,540,000
51
[1, 51]
60,141,180,000
52
[1, 51]
60,142,140,000
53
[1, 51]
61,435,320,000
54
[1, 147]
61,440,180,000
55
[1, 147]
61,447,320,000
56
[1, 4]
61,683,780,000
57
[1, 23]
62,045,580,000
58
[65, 1]
62,119,620,000
59
[41, 51, 29, 1]
62,301,000,000
60
[1, 51]
63,490,800,000
61
[1, 29]
64,533,300,000
62
[41, 51, 29, 1]
65,221,320,000
64
[41, 51, 1]
65,829,660,000
65
[97, 1]
65,833,680,000
67
[57, 1]
66,538,740,000
68
[1, 63]
67,050,720,000
69
[1, 133, 41, 29, 62]
67,126,020,000
70
[1, 51]
67,469,340,000
71
[1, 125]
68,346,660,000
72
[1, 51]
68,670,660,000
73
[41, 51, 1]
68,671,080,000
74
[41, 1]
69,196,620,000
75
[1, 63]
69,213,540,000
76
[41, 1]
69,451,800,000
77
[41, 51, 1]
69,543,240,000
78
[41, 1]
69,894,600,000
79
[41, 51, 1]
69,966,420,000
80
[8, 1]
70,167,120,000
81
[41, 1]
70,403,880,000
82
[112, 1]
70,434,960,000
83
[41, 1]
70,509,900,000
84
[41, 51, 1]
70,571,280,000
85
[1, 29]
71,017,320,000
86
[1, 29]
71,020,200,000
87
[1, 142]
71,021,460,000
88
[41, 51, 1]
71,180,220,000
89
[1, 51]
71,810,640,000
90
[1, 51]
71,812,200,000
91
[41, 51, 29, 1]
71,872,680,000
92
[136, 1]
71,968,200,000
93
[1, 51]
71,979,660,000
94
[41, 1]
72,817,080,000
95
[1, 63]
72,916,680,000
96
[1, 4]
73,011,180,000
97
[120, 1]
73,438,860,000
98
[1, 62]
73,540,440,000
99
[120, 1]
73,712,100,000
100
[41, 51, 29, 1]
74,642,160,000
101
[1, 63]
74,659,500,000
102
[136, 1]
74,891,160,000

Source Paper: https://arxiv.org/abs/1802.06916

Usage

from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset

dataset = CornellTemporalHyperGraphDataset(root = "./", name="email-Enron", split="train")

Citation

@article{Benson-2018-simplicial,
 author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon},
 title = {Simplicial closure and higher-order link prediction},
 year = {2018},
 doi = {10.1073/pnas.1800683115},
 publisher = {National Academy of Sciences},
 issn = {0027-8424},
 journal = {Proceedings of the National Academy of Sciences}
}
Downloads last month
67

Models trained or fine-tuned on SauravMaheshkar/email-Enron

Collection including SauravMaheshkar/email-Enron