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Table 1 Summary statistics about empirical and synthetic time-varying graph data. In order: number of single nodes \(|\mathcal{V}|\), number of steps \(|\mathcal{T}|\), number of events \(|\mathcal{E}|\), number of active nodes \(|\mathcal{V}^{(\mathcal{T})}|\), average weight of events \(\frac{1}{|\mathcal{E}|}\sum_{e \in \mathcal{E}} \omega (e)\), nodes density \(\frac{|\mathcal{V}^{(\mathcal{T})}|}{|\mathcal{V}||\mathcal{T}|}\) and links density \(\frac{2|\mathcal{E}|}{|\mathcal{V}|(|\mathcal{V}|-1)|\mathcal{T}|}\)

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