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Table 2 Summary of structural characteristics of the network datasets: type of the network, number of layers (l), number of entities, or actors (a), number of nodes (n), number of edges (e), density mean/SD over the layers, node degree mean/SD over the layers, average entity frequency (aef), homophily, and number of classes (c)

From: Graph convolutional and attention models for entity classification in multilayer networks

Network Type l a n e density degree aef (%) homophily c
Balance Social 4 625 2500 155,000 \(0.199 \pm 0.0\) \(124.0 \pm 0.0\) 100 \(0.507 \pm 0.0\) 3
CKM-Social Social 3 241 723 2740 \(0.016 \pm 0.001\) \(7.580 \pm 0.632\) 100 \(1.0 \pm 0.0\) 4
Congress Vote 16 435 6960 707,872 \(0.496 \pm 0.025\) \(203.411 \pm 10.86\) 100 \(0.670 \pm 0.115\) 2
DKPol Political 3 490 839 20,226 \(0.07 \pm 0.008\) \(28.991 \pm 44.115\) 57.07 \(0.518 \pm 0.282\) 10
Leskovec-Ng Collaboration, Temporal 4 191 214 536 \(0.166 \pm 0.151\) \(4.088 \pm 1.454\) 28.01 \(0.994 \pm 0.012\) 2
Starwars Interaction 6 92 157 494 \(0.253 \pm 0.045\) \(6.117\pm 0.835\) 28.44 \(0.571 \pm 0.067\) 3
Terrorist-Noordin Terrorist 4 79 227 1822 \(0.129 \pm 0.05\) \(13.829 \pm 9.087\) 71.83 \(0.780 \pm 0.118\) 2
Terrorist-status Terrorist 4 79 227 1822 \(0.129 \pm 0.05\) \(13.829 \pm 9.087\) 71.83 \(0.469 \pm 0.069\) 3
Vickers Social 3 29 87 740 \(0.304 \pm 0.122\) \(17.011 \pm 6.854\) 100 \(0.763 \pm 0.088\) 2
Koumbia-2-mpx Geospatial 2 2246 2246 6011 \(0.002 \pm 0.001\) \(5.355 \pm 0.017\) 50 \(0.814 \pm 0.004\) 2
Koumbia-5-mpx Geospatial 5 2246 3776 12,870 \(0.005 \pm 0.002\) \(6.751 \pm 0.448\) 33.62 \(0.827 \pm 0.097\) 2
Koumbia-10-mpx Geospatial 10 2246 7191 27,433 \(0.006 \pm 0.003\) \(7.567 \pm 0.392\) 32.02 \(0.826 \pm 0.091\) 2
Koumbia-15-mpx Geospatial 15 2246 10,591 43,285 \(0.007 \pm 0.003\) \(8.103 \pm 0.458\) 31.43 \(0.826 \pm 0.090\) 2
Koumbia-20-mpx Geospatial 20 2246 14,069 59,968 \(0.008 \pm 0.07\) \(8.447 \pm 0.489\) 31.32 \(0.823 \pm 0.089\) 2