From: Time-varying graph representation learning via higher-order skip-gram with negative sampling
Dataset | \(|\mathcal{V}|\) | \(|\mathcal{T}|\) | \(|\mathcal{E}|\) | \(|\mathcal{V}^{(\mathcal{T})}|\) | Average weight | Nodes density | Links density |
---|---|---|---|---|---|---|---|
LyonSchool | 242 | 104 | 44,820 | 17,174 | 2.806 | 0.6824 | 0.0148 |
SFHH | 403 | 127 | 17,223 | 10,815 | 4.079 | 0.2113 | 0.0017 |
LH10 | 76 | 321 | 7435 | 4880 | 4.448 | 0.2000 | 0.0081 |
Thiers13 | 327 | 246 | 35,862 | 32,546 | 5.256 | 0.4046 | 0.0027 |
InVS15 | 217 | 691 | 18,791 | 22,451 | 4.164 | 0.1497 | 0.0012 |
OpenABM-2k-100 | 2000 | 100 | 1,243,551 | 198,537 | 1.0 | 0.9927 | 0.0062 |
OpenABM-5k-20 | 5000 | 20 | 632,523 | 99,966 | 1.0 | 0.9997 | 0.0025 |