HyperGraph Datasets
Collection
Collection of HyperGraph Datasets
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17 items
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Updated
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7
hyperedge
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[1]
| 3,547,497,600,000
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2
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[2]
| 1,594,425,600,000
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6
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[5]
| 3,499,459,200,000
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7
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[5]
| 3,499,459,200,000
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8
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[6]
| 3,193,689,600,000
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9
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[7]
| 3,247,257,600,000
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10
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[7]
| 3,247,257,600,000
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11
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[7]
| 3,247,257,600,000
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12
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[8, 6]
| 3,385,065,600,000
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13
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[8, 6]
| 3,281,212,800,000
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14
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[8, 6]
| 3,281,212,800,000
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[8, 6]
| 3,281,212,800,000
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16
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[8, 6]
| 3,281,212,800,000
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17
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[9]
| 3,302,294,400,000
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18
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[7]
| 3,247,257,600,000
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19
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[7]
| 3,247,257,600,000
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20
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[9]
| 3,302,294,400,000
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21
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[7]
| 3,317,328,000,000
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22
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[7]
| 3,317,328,000,000
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23
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[9]
| 3,472,502,400,000
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24
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[8]
| 3,076,012,800,000
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25
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[8]
| 3,053,116,800,000
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26
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[8]
| 3,053,116,800,000
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27
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[8]
| 3,053,116,800,000
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28
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[10]
| 3,093,033,600,000
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29
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[10]
| 3,093,033,600,000
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30
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[8]
| 3,156,451,200,000
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31
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[8]
| 3,192,393,600,000
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32
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[8]
| 3,168,806,400,000
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33
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[8]
| 3,168,806,400,000
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34
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[8]
| 3,208,291,200,000
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35
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[8]
| 3,208,291,200,000
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36
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[11]
| 3,278,793,600,000
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37
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[11]
| 3,278,793,600,000
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38
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[11]
| 3,278,793,600,000
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39
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[11]
| 3,408,652,800,000
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40
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[12]
| 3,456,172,800,000
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41
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[12]
| 3,456,172,800,000
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42
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[13]
| 2,964,988,800,000
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44
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[15]
| 3,041,107,200,000
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45
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[15]
| 3,041,107,200,000
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50
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[8]
| 3,289,766,400,000
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51
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[17]
| 3,284,841,600,000
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52
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[17]
| 3,398,112,000,000
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53
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[18]
| 3,607,027,200,000
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54
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[18]
| 3,607,027,200,000
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62
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[22]
| 3,247,257,600,000
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75
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[24]
| 3,439,756,800,000
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76
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[25]
| 3,452,803,200,000
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77
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[26]
| 3,517,084,800,000
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78
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[25]
| 3,452,803,200,000
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79
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[27]
| 3,360,355,200,000
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80
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[27]
| 3,360,355,200,000
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81
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[27]
| 3,360,355,200,000
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82
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[28]
| 3,452,803,200,000
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83
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[27]
| 3,421,094,400,000
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84
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[27]
| 3,497,212,800,000
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85
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[27]
| 3,497,212,800,000
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86
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[29]
| 3,565,641,600,000
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87
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[29]
| 3,565,641,600,000
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88
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[30]
| 3,321,043,200,000
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89
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[30]
| 3,321,043,200,000
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90
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[30]
| 3,321,043,200,000
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91
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[31]
| 2,981,404,800,000
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92
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[31]
| 2,981,404,800,000
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93
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[31]
| 2,981,404,800,000
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94
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[31]
| 2,981,404,800,000
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95
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[31]
| 3,050,956,800,000
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96
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[32]
| 3,439,756,800,000
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97
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[32]
| 3,520,886,400,000
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98
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[33]
| 3,479,068,800,000
|
99
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[33]
| 3,479,068,800,000
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101
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[35]
| 3,510,000,000,000
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102
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[35]
| 3,510,000,000,000
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103
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[33]
| 3,479,068,800,000
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104
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[33]
| 3,479,068,800,000
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105
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[32]
| 3,520,886,400,000
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106
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[32]
| 3,520,886,400,000
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107
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[36]
| 3,265,401,600,000
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108
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[36]
| 3,265,401,600,000
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109
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[36]
| 3,265,401,600,000
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115
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[39]
| 3,452,803,200,000
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116
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[39]
| 3,452,803,200,000
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117
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[39]
| 3,452,803,200,000
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118
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[28]
| 3,452,803,200,000
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119
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[28]
| 3,452,803,200,000
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120
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[28]
| 3,452,803,200,000
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121
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[40]
| 3,194,812,800,000
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122
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[40]
| 3,460,406,400,000
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123
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[41]
| 2,379,888,000,000
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124
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[41]
| 2,379,888,000,000
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126
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[41]
| 2,379,888,000,000
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127
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[43]
| 3,235,680,000,000
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128
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[43]
| 3,515,875,200,000
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129
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[44]
| 3,312,230,400,000
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130
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[44]
| 3,027,196,800,000
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131
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[45]
| 3,501,532,800,000
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132
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[46]
| 3,008,448,000,000
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133
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[46]
| 3,075,667,200,000
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134
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[46]
| 3,116,188,800,000
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Source Paper: https://arxiv.org/abs/1802.06916
from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset
dataset = CornellTemporalHyperGraphDataset(root = "./", name="NDC-substances-25", split="train")
@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}
}