HyperGraph Datasets
Collection
Collection of HyperGraph Datasets
•
17 items
•
Updated
•
7
Datasets:
Dataset Viewer
hyperedge
int64 1
49.7k
| nodes
stringclasses 446
values | timestamp
float64 0
3,573B
|
|---|---|---|
1 |
[1, 2]
| 3,547,497,600,000 |
6 |
[11, 12, 13]
| 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 |
[16, 17]
| 3,247,257,600,000 |
12 |
[18, 14, 15]
| 3,385,065,600,000 |
13 |
[18, 14, 15]
| 3,281,212,800,000 |
14 |
[18, 14, 15]
| 3,281,212,800,000 |
15 |
[18, 14, 15]
| 3,281,212,800,000 |
16 |
[18, 14, 15]
| 3,281,212,800,000 |
17 |
[17, 19, 15]
| 3,302,294,400,000 |
18 |
[16, 17]
| 3,247,257,600,000 |
19 |
[16, 17]
| 3,247,257,600,000 |
20 |
[17, 19, 15]
| 3,302,294,400,000 |
21 |
[16, 17]
| 3,317,328,000,000 |
22 |
[16, 17]
| 3,317,328,000,000 |
23 |
[17, 19, 15]
| 3,472,502,400,000 |
24 |
[18]
| 3,076,012,800,000 |
25 |
[18]
| 3,053,116,800,000 |
26 |
[18]
| 3,053,116,800,000 |
27 |
[18]
| 3,053,116,800,000 |
28 |
[20, 21]
| 3,093,033,600,000 |
29 |
[20, 21]
| 3,093,033,600,000 |
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 |
36 |
[22, 23]
| 3,278,793,600,000 |
37 |
[22, 23]
| 3,278,793,600,000 |
38 |
[22, 23]
| 3,278,793,600,000 |
39 |
[22, 23]
| 3,408,652,800,000 |
40 |
[24, 25, 26]
| 3,456,172,800,000 |
41 |
[24, 25, 26]
| 3,456,172,800,000 |
42 |
[27, 28]
| 3,041,107,200,000 |
43 |
[27, 28]
| 3,041,107,200,000 |
44 |
[29, 30]
| 3,043,699,200,000 |
45 |
[29, 30]
| 3,154,809,600,000 |
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 |
59 |
[29, 39]
| 2,974,060,800,000 |
60 |
[29, 30]
| 3,398,025,600,000 |
61 |
[29, 30]
| 3,398,025,600,000 |
62 |
[29, 30]
| 3,398,025,600,000 |
68 |
[48, 49, 50, 47]
| 3,517,084,800,000 |
76 |
[56, 57]
| 3,565,641,600,000 |
77 |
[56, 57]
| 3,565,641,600,000 |
78 |
[58, 59, 60]
| 3,321,043,200,000 |
79 |
[58, 59, 60]
| 3,321,043,200,000 |
80 |
[58, 59, 60]
| 3,321,043,200,000 |
81 |
[59, 60, 61]
| 2,981,404,800,000 |
82 |
[59, 60, 61]
| 2,981,404,800,000 |
83 |
[59, 60, 61]
| 2,981,404,800,000 |
84 |
[59, 60, 61]
| 2,981,404,800,000 |
85 |
[59, 60, 61]
| 3,050,956,800,000 |
86 |
[64, 62, 63]
| 3,439,756,800,000 |
87 |
[64, 62, 63]
| 3,520,886,400,000 |
88 |
[65, 66]
| 3,479,068,800,000 |
89 |
[65, 66]
| 3,479,068,800,000 |
91 |
[72, 73, 71]
| 3,510,000,000,000 |
92 |
[72, 73, 71]
| 3,510,000,000,000 |
93 |
[65, 66]
| 3,479,068,800,000 |
94 |
[65, 66]
| 3,479,068,800,000 |
95 |
[64, 62, 63]
| 3,520,886,400,000 |
96 |
[64, 62, 63]
| 3,520,886,400,000 |
97 |
[74, 75, 76, 77, 78, 79, 53]
| 3,265,401,600,000 |
98 |
[74, 75, 76, 77, 78, 79, 53]
| 3,265,401,600,000 |
99 |
[74, 75, 76, 77, 78, 79, 53]
| 3,265,401,600,000 |
105 |
[83, 84]
| 3,452,803,200,000 |
106 |
[83, 84]
| 3,452,803,200,000 |
107 |
[83, 84]
| 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 |
115 |
[59, 87]
| 3,247,862,400,000 |
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]
| 3,008,448,000,000 |
123 |
[94]
| 3,075,667,200,000 |
124 |
[94]
| 3,116,188,800,000 |
125 |
[94]
| 3,111,868,800,000 |
126 |
[96, 95]
| 3,243,715,200,000 |
127 |
[96, 95]
| 3,510,345,600,000 |
128 |
[96, 95]
| 3,529,094,400,000 |
129 |
[96, 95]
| 3,529,094,400,000 |
136 |
[27, 28]
| 3,102,883,200,000 |
137 |
[27, 28]
| 3,102,883,200,000 |
138 |
[100, 101, 102]
| 2,545,862,400,000 |
139 |
[100, 101, 102]
| 2,404,339,200,000 |
140 |
[100, 101, 102]
| 2,991,081,600,000 |
End of preview. Expand
in Data Studio
Source Paper: https://arxiv.org/abs/1802.06916
Usage
from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset
dataset = CornellTemporalHyperGraphDataset(root = "./", name="NDC-classes-25", 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}
}
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Size of downloaded dataset files:
2.94 MB
Size of the auto-converted Parquet files:
997 kB
Number of rows:
75,994