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Table 4 Classification performance on networks with different sparsity (mean \(\pm\) std)

From: GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification

Threshold Number of edges Sparisty Accuracy Sensitivity Specificity F1 AUC
0.1 10,056 0.1689 0.6686 ± 0.0344 0.7287 ± 0.0358 0.6113 ± 0.0790 0.6826 ± 0.0236 0.7396 ± 0.0317
0.2 7976 0.3408 0.6512 ± 0.0604 0.7327 ± 0.0360 0.5736 ± 0.1110 0.6738 ± 0.0433 0.7165 ± 0.0603
0.3 5877 0.5142 0.6377 ± 0.0684 0.7149 ± 0.0449 0.5642 ± 0.1255 0.6600 ± 0.0467 0.7005 ± 0.0648
0.4 3933 0.6750 0.6232 ± 0.0693 0.7129 ± 0.0429 0.5377 ± 0.1303 0.6506 ± 0.0460 0.6878 ± 0.0681
0.5 2330 0.8074 0.6145 ± 0.0516 0.6594 ± 0.0409 0.5717 ± 0.0992 0.6263 ± 0.0370 0.6831 ± 0.0542
Dense network 12,100 0 0.6802 ± 0.0269 0.7406 ± 0.0408 0.6226 ± 0.0534 0.6931 ± 0.0248 0.7358 ± 0.0373
  1. The bold means it is the best result for each metric (column of the table)