Table 4 The average MSE of predicting randomly distributed test values using different auto-encoder models
From: A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks
Model | E. coli | Mus Musclus | ||
|---|---|---|---|---|
TF_Net | PPI | Genetics | PPI | |
GCN | 0.043 ± 0.00175 | 0.065 ± 0.004 | 0.114 ± 0.004 | 0.011 ± 0.001 |
GraphSAGE | 0.027 ± 0.0007 | 0.023 ± 0.0004 | 0.026 ± 0.0003 | 0.004 ± 0.0006 |
GraphConv | 0.041 ± 0.003 | 0.068 ± 0.05 | 0.182 ± 0.046 | 2.06 ± 2.73 |
FeatGraphConv (our) | 0.025 ± 0.0008 | 0.023 ± 0.0006 | 0.025 ± 0.0004 | 0.003 ± 0.0002 |
MLP Auto-encoder | 0.031 ± 0.0007 | 0.028 ± 0.0003 | 0.027 ± 0.0004 | 0.004 ± 0.0005 |
MAGIC | 3.505 ± 0.006 | 3.661 ± 0.017 | 3.215 ± 0.003 | 0.050 ± 0.0002 |