Datasets:

SauravMaheshkar
/

NDC-substances

hyperedge int64 1 113k	nodes stringlengths 3 180	timestamp float64 0 3,612B
1	[1]	3,547,497,600,000
2	[2]	1,594,425,600,000
8	[6]	3,193,689,600,000
9	[7]	3,247,257,600,000
10	[7]	3,247,257,600,000
11	[7]	3,247,257,600,000
12	[8, 6]	3,385,065,600,000
13	[8, 6]	3,281,212,800,000
14	[8, 6]	3,281,212,800,000
15	[8, 6]	3,281,212,800,000
16	[8, 6]	3,281,212,800,000
17	[9]	3,302,294,400,000
18	[7]	3,247,257,600,000
19	[7]	3,247,257,600,000
20	[9]	3,302,294,400,000
21	[7]	3,317,328,000,000
22	[7]	3,317,328,000,000
23	[9]	3,472,502,400,000
24	[8]	3,076,012,800,000
25	[8]	3,053,116,800,000
26	[8]	3,053,116,800,000
27	[8]	3,053,116,800,000
28	[10]	3,093,033,600,000
29	[10]	3,093,033,600,000
30	[8]	3,156,451,200,000
31	[8]	3,192,393,600,000
32	[8]	3,168,806,400,000
33	[8]	3,168,806,400,000
34	[8]	3,208,291,200,000
35	[8]	3,208,291,200,000
42	[13]	2,964,988,800,000
44	[15]	3,041,107,200,000
45	[15]	3,041,107,200,000
46	[16]	3,043,699,200,000
47	[16]	3,154,809,600,000
48	[16]	3,154,809,600,000
49	[16]	3,096,921,600,000
50	[8]	3,289,766,400,000
51	[17]	3,284,841,600,000
52	[17]	3,398,112,000,000
53	[18]	3,607,027,200,000
54	[18]	3,607,027,200,000
56	[16]	3,043,699,200,000
60	[21]	2,634,508,800,000
61	[21]	2,634,508,800,000
62	[22]	3,247,257,600,000
63	[21]	2,974,060,800,000
64	[21]	2,823,811,200,000
65	[16]	3,398,025,600,000
66	[16]	3,398,025,600,000
67	[16]	3,398,025,600,000
68	[21]	3,592,771,200,000
69	[21]	3,592,771,200,000
75	[24]	3,439,756,800,000
77	[26]	3,517,084,800,000
79	[27]	3,360,355,200,000
80	[27]	3,360,355,200,000
81	[27]	3,360,355,200,000
82	[28]	3,452,803,200,000
83	[27]	3,421,094,400,000
84	[27]	3,497,212,800,000
85	[27]	3,497,212,800,000
86	[29]	3,565,641,600,000
87	[29]	3,565,641,600,000
88	[30]	3,321,043,200,000
89	[30]	3,321,043,200,000
90	[30]	3,321,043,200,000
91	[31]	2,981,404,800,000
92	[31]	2,981,404,800,000
93	[31]	2,981,404,800,000
94	[31]	2,981,404,800,000
95	[31]	3,050,956,800,000
96	[32]	3,439,756,800,000
97	[32]	3,520,886,400,000
98	[33]	3,479,068,800,000
99	[33]	3,479,068,800,000
101	[35]	3,510,000,000,000
102	[35]	3,510,000,000,000
103	[33]	3,479,068,800,000
104	[33]	3,479,068,800,000
105	[32]	3,520,886,400,000
106	[32]	3,520,886,400,000
115	[39]	3,452,803,200,000
116	[39]	3,452,803,200,000
117	[39]	3,452,803,200,000
118	[28]	3,452,803,200,000
119	[28]	3,452,803,200,000
120	[28]	3,452,803,200,000
123	[41]	2,379,888,000,000
124	[41]	2,379,888,000,000
125	[42]	3,247,862,400,000
126	[41]	2,379,888,000,000
127	[43]	3,235,680,000,000
128	[43]	3,515,875,200,000
129	[44]	3,312,230,400,000
130	[44]	3,027,196,800,000
131	[45]	3,501,532,800,000
132	[46]	3,008,448,000,000
133	[46]	3,075,667,200,000
134	[46]	3,116,188,800,000

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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-substances", 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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Collection of HyperGraph Datasets • 17 items • Updated Apr 4 • 7