SauravMaheshkar/CATWalk-NDC-classes
Graph Machine Learning
•
Updated
Datasets:
hyperedge
int64 1
49.7k
| nodes
stringclasses 523
values | timestamp
float64 172B
3,573B
|
|---|---|---|
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19 | [16, 17] | 3,247,257,600,000 |
21 | [16, 17] | 3,317,328,000,000 |
22 | [16, 17] | 3,317,328,000,000 |
24 | [18] | 3,076,012,800,000 |
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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] | 3,043,699,200,000 |
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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 |
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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 |
108 | [54, 55] | 3,452,803,200,000 |
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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 |
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 |
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 |
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Source Paper: https://arxiv.org/abs/1802.06916
Usage
from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset
dataset = CornellTemporalHyperGraphDataset(root = "./", name="NDC-classes", 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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