HyperGraph Datasets
Collection
Collection of HyperGraph Datasets
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17 items
•
Updated
•
7
hyperedge
int64 1
49.7k
| nodes
stringclasses 523
values | timestamp
float64 172B
3,573B
|
---|---|---|
1 |
[1, 2]
| 3,547,497,600,000 |
6 |
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| 3,499,459,200,000 |
7 |
[11, 12, 13]
| 3,499,459,200,000 |
8 |
[14, 15]
| 3,193,689,600,000 |
9 |
[16, 17]
| 3,247,257,600,000 |
10 |
[16, 17]
| 3,247,257,600,000 |
11 |
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| 3,247,257,600,000 |
12 |
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| 3,385,065,600,000 |
13 |
[18, 14, 15]
| 3,281,212,800,000 |
14 |
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| 3,281,212,800,000 |
15 |
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| 3,281,212,800,000 |
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| 3,281,212,800,000 |
18 |
[16, 17]
| 3,247,257,600,000 |
19 |
[16, 17]
| 3,247,257,600,000 |
21 |
[16, 17]
| 3,317,328,000,000 |
22 |
[16, 17]
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24 |
[18]
| 3,076,012,800,000 |
25 |
[18]
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26 |
[18]
| 3,053,116,800,000 |
27 |
[18]
| 3,053,116,800,000 |
30 |
[18]
| 3,156,451,200,000 |
31 |
[18]
| 3,192,393,600,000 |
32 |
[18]
| 3,168,806,400,000 |
33 |
[18]
| 3,168,806,400,000 |
34 |
[18]
| 3,208,291,200,000 |
35 |
[18]
| 3,208,291,200,000 |
44 |
[29, 30]
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45 |
[29, 30]
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46 |
[29, 30]
| 3,154,809,600,000 |
47 |
[29, 30]
| 3,096,921,600,000 |
48 |
[18]
| 3,289,766,400,000 |
49 |
[32, 31]
| 3,284,841,600,000 |
50 |
[32, 31]
| 3,398,112,000,000 |
54 |
[29, 30]
| 3,043,699,200,000 |
58 |
[37, 38]
| 3,247,257,600,000 |
60 |
[29, 30]
| 3,398,025,600,000 |
61 |
[29, 30]
| 3,398,025,600,000 |
62 |
[29, 30]
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67 |
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| 3,517,084,800,000 |
69 |
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70 |
[51, 52, 53]
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73 |
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| 3,421,094,400,000 |
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[51, 52, 53]
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[51, 52, 53]
| 3,497,212,800,000 |
78 |
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80 |
[58, 59, 60]
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| 2,981,404,800,000 |
82 |
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84 |
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| 2,981,404,800,000 |
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[59, 60, 61]
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[64, 62, 63]
| 3,520,886,400,000 |
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[74, 75, 76, 77, 78, 79, 53]
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[74, 75, 76, 77, 78, 79, 53]
| 3,265,401,600,000 |
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[74, 75, 76, 77, 78, 79, 53]
| 3,265,401,600,000 |
108 |
[54, 55]
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109 |
[54, 55]
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110 |
[54, 55]
| 3,452,803,200,000 |
111 |
[59, 85, 86, 87]
| 3,194,812,800,000 |
112 |
[59, 85, 86, 87]
| 3,460,406,400,000 |
113 |
[88, 89]
| 2,379,888,000,000 |
114 |
[88, 89]
| 2,379,888,000,000 |
115 |
[59, 87]
| 3,247,862,400,000 |
116 |
[88, 89]
| 2,379,888,000,000 |
117 |
[90, 91]
| 3,235,680,000,000 |
118 |
[90, 91]
| 3,515,875,200,000 |
119 |
[74, 75, 78]
| 3,312,230,400,000 |
120 |
[74, 75, 78]
| 3,027,196,800,000 |
121 |
[92, 93]
| 3,501,532,800,000 |
122 |
[94]
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123 |
[94]
| 3,075,667,200,000 |
124 |
[94]
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125 |
[94]
| 3,111,868,800,000 |
126 |
[96, 95]
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127 |
[96, 95]
| 3,510,345,600,000 |
128 |
[96, 95]
| 3,529,094,400,000 |
129 |
[96, 95]
| 3,529,094,400,000 |
138 |
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139 |
[100, 101, 102]
| 2,404,339,200,000 |
140 |
[100, 101, 102]
| 2,991,081,600,000 |
141 |
[100, 101, 102]
| 2,991,081,600,000 |
142 |
[59, 85, 86, 87]
| 2,823,552,000,000 |
143 |
[103, 104, 105, 106, 107, 108]
| 3,477,340,800,000 |
145 |
[112, 111]
| 2,776,723,200,000 |
146 |
[90, 91]
| 3,021,321,600,000 |
147 |
[113, 114]
| 3,057,609,600,000 |
149 |
[115, 116]
| 3,091,478,400,000 |
Source Paper: https://arxiv.org/abs/1802.06916
from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset
dataset = CornellTemporalHyperGraphDataset(root = "./", name="NDC-classes", 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}
}