Table 3.
Comparison of performance between our proposed non-local GNN and other classical methods.
Method | Accuracy | Precision | Recall | F-measure | AUC |
---|---|---|---|---|---|
DC | 0.7335 | 0.4050 | 0.3470 | 0.3737 | 0.5977 |
CC | 0.7150 | 0.3580 | 0.3067 | 0.3304 | 0.5716 |
BC | 0.7139 | 0.3550 | 0.3041 | 0.3276 | 0.5699 |
EC | 0.7194 | 0.3690 | 0.3161 | 0.3405 | 0.5777 |
LAC | 0.7563 | 0.4630 | 0.3967 | 0.4273 | 0.6299 |
NC | 0.7469 | 0.4390 | 0.3761 | 0.4051 | 0.6166 |
PeC | 0.7555 | 0.4610 | 0.3950 | 0.4254 | 0.6288 |
WDC | 0.7630 | 0.4800 | 0.4113 | 0.4430 | 0.6394 |
PSGN | 0.7614 | 0.4771 | 0.4301 | 0.4524 | 0.6450 |
LSTM-AM | 0.7340 | 0.4688 | 0.5674 | 0.5134 | 0.6781 |
BiLSTM | 0.7369 | 0.4803 | 0.5742 | 0.5231 | 0.6829 |
Bold values mean best scores.