Table 2 Results with CML model
From: A general deep learning framework for network reconstruction and dynamics learning
Node Num | State | LSTM | NRI | GGN | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
MSE | ACC | TPR | FPR | MSE | ACC | TPR | FPR | MSE | ||
10 | non-chaotic | 1.92e-2 | 0.531 | 0.446 | 0.588 | 1.69e-4 | 1 | 1 | 0 | 5.63e-6 |
10 | chaotic | 2.54e-2 | 0.547 | 0.459 | 0.605 | 4.04e-4 | 0.993 | 1 | 0.013 | 3.24e-5 |
30 | non-chaotic | 4.11e-2 | - | - | - | - | 1 | 1 | 0 | 3.29e-6 |
30 | chaotic | 5.03e-2 | - | - | - | - | 0.999 | 1 | 0.0017 | 3.41e-6 |