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Table 11 Performance of ML-GAT over the Koumbia multilayer networks with varying number of layers, and different types of input features

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

\(\ell\) Koumbia-Fon Koumbia-Foff Koumbia-normal
Accuracy MRR Accuracy MRR Accuracy MRR
2 \(\mathbf {0.9082 \pm 0.0113}\) \(\mathbf {0.9541\pm 0.0057}\) \(0.7426 \pm 0.0200\) \(0.8713 \pm 0.0100\) \(0.7556 \pm 0.0164\) \(0.8778 \pm 0.0082\)
5 \(\mathbf {0.9373 \pm 0.0054}\) \(\mathbf {0.9687 \pm 0.0027}\) \(0.7656 \pm 0.0117\) \(0.8828 \pm 0.0059\) \(0.8355 \pm 0.01153\) \(0.9193 \pm 0.0076\)
10 \(\mathbf {0.9385 \pm 0.0073}\) \(\mathbf {0.9693 \pm 0.0036}\) \(0.7588 \pm 0.0126\) \(0.8794 \pm 0.0063\) \(0.7874 \pm 0.0116\) \(0.8937 \pm 0.0058\)
15 \(\mathbf {0.9355 \pm 0.0089}\) \(\mathbf {0.9687 \pm 0.0044}\) \(0.7522 \pm 0.0125\) \(0.8761 \pm 0.0063\) \(0.7723 \pm 0.0133\) \(0.8826 \pm 0.0067\)
20 \(\mathbf {0.9404 \pm 0.0069}\) \(\mathbf {0.9702 \pm 0.0035}\) \(0.7488 \pm 0.0167\) \(0.8744 \pm 0.0084\) \(0.7706 \pm 0.0091\) \(0.8853 \pm 0.0046\)
  1. Bold values correspond to best performances