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Table 5 Accuracy (mean and standard deviation over 10 runs) obtained by the proposed methods and competitors

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

Network

ML-GAT

ML-GCN

GrAMME-SG

GrAMME-Fusion

GAT

GCN

Balance

\(0.8582 \pm 0.0283\)

\(\mathbf {0.9306 \pm 0.0216}\)

\(0.8004 \pm 0.0244\)

\(0.8230 \pm 0.0764\)

\(0.6445\pm 0.0720\)

\(0.8101 \pm 0.0533\)

CKM-Social

\(0.9544 \pm 0.0113\)

\(0.8242 \pm 0.0420\)

\(0.9685 \pm 0.0138\)

\(0.9807 \pm 0.0075\)

\(\mathbf {0.9962 \pm 0.0052}\)

\(0.9736 \pm 0.0121\)

Congress

\(0.9236 \pm 0.0275\)

\(0.9362 \pm 0.0217\)

\(0.8834 \pm 0.0000\)

\(\mathbf {0.9601\pm 0.0000}\)

\(0.5370 \pm 0.0953\)

\(0.6146 \pm 0.0000\)

DKPol

\(0.8332 \pm 0.0163\)

\(0.7866 \pm 0.0237\)

\(0.5590 \pm 0.0256\)

\(0.8424 \pm 0.0212\)

\(0.8535 \pm 0.0206\)

\(\mathbf {0.8838 \pm 0.0151}\)

Leskovec-Ng

\(\mathbf {0.9959 \pm 0.0036}\)

\(0.9243 \pm 0.0405\)

\(0.9545 \pm 0.0422\)

\(0.9797 \pm 0.0022\)

\(0.9903 \pm 0.0128\)

\(0.9937 \pm 0.0022\)

Starwars

\(0.7000 \pm 0.0788\)

\(0.7140 \pm 0.0477\)

\(0.6319 \pm 0.0771\)

\(\mathbf {0.7754 \pm 0.0370}\)

\(0.7464 \pm 0.0300\)

\(0.6855 \pm 0.0611\)

Terrorist-Noordin

\(0.7150 \pm 0.0669\)

\(0.7833 \pm 0.0801\)

\(0.7000 \pm 0.0939\)

\(\mathbf {0.8237 \pm 0.0322}\)

\(0.7133 \pm 0.0706\)

\(0.6667 \pm 0.0720\)

Terrorist-status

\(0.4767 \pm 0.0439\)

\(0.5022 \pm 0.0835\)

\(0.4915 \pm 0.0554\)

\(0.4932 \pm 0.0847\)

\(0.4650 \pm 0.0748\)

\(\mathbf {0.5034 \pm 0.0809}\)

Vickers

\(\mathbf {0.9692 \pm 0.0372}\)

\(0.9591 \pm 0.0725\)

\(0.8227 \pm 0.1311\)

\(0.8909 \pm 0.1563\)

\(0.9136\pm 0.0693\)

\(0.8227 \pm 0.0756\)

Koumbia-2-mpx

\(\mathbf {0.7937 \pm 0.0106}\)

\(0.7578 \pm 0.0434\)

\(0.6663 \pm 0.0170\)

\(0.7520 \pm 0.0064\)

\(0.7485 \pm 0.0231\)

\(0.7031 \pm 0.0107\)

Koumbia-5-mpx

\(\mathbf {0.8404 \pm 0.0132}\)

\(0.8314 \pm 0.0289\)

\(0.6425 \pm 0.0000\)

\(0.8040 \pm 0.0000\)

\(0.8077 \pm 0.0128\)

\(0.7802 \pm 0.0167\)

Koumbia-10-mpx

\(\mathbf {0.8425 \pm 0.0093}\)

\(0.8192 \pm 0.0011\)

na

na

\(0.8420 \pm 0.0058\)

\(0.8147 \pm 0.0094\)

  1. Bold values refer to the best results on each network