SauravMaheshkar/CATWalk-NDC-classes
Graph Machine Learning
•
Updated
Datasets:
hyperedge
int64 1
49.7k
| nodes
stringclasses 523
values | timestamp
float64 172B
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 |
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] | 3,317,328,000,000 |
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 |
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 |
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 |
60 | [29, 30] | 3,398,025,600,000 |
61 | [29, 30] | 3,398,025,600,000 |
62 | [29, 30] | 3,398,025,600,000 |
67 | [45, 46] | 3,439,756,800,000 |
68 | [48, 49, 50, 47] | 3,517,084,800,000 |
69 | [51, 52, 53] | 3,360,355,200,000 |
70 | [51, 52, 53] | 3,360,355,200,000 |
71 | [51, 52, 53] | 3,360,355,200,000 |
72 | [54, 55] | 3,452,803,200,000 |
73 | [51, 52, 53] | 3,421,094,400,000 |
74 | [51, 52, 53] | 3,497,212,800,000 |
75 | [51, 52, 53] | 3,497,212,800,000 |
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 |
108 | [54, 55] | 3,452,803,200,000 |
109 | [54, 55] | 3,452,803,200,000 |
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] | 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 |
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 |
End of preview. Expand
in Dataset Viewer.
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}
}
- Downloads last month
- 51
Size of downloaded dataset files:
3.27 MB
Size of the auto-converted Parquet files:
1.07 MB
Number of rows:
82,459
Models trained or fine-tuned on SauravMaheshkar/NDC-classes