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Why do I get different results when I set random seeds before and after the dataset? Taking the GCN model as an example, using the CORA dataset after meta attack (ptd_rate=0.05), the model's performance differs greatly when I set random seeds before and after the dataset.
The text was updated successfully, but these errors were encountered:
I am not exactly sure about the described scenario, but we are supposed to use the same data split before and after attack. This ensures that the attacker uses the correct supervision to learn the perturbation. Otherwise, the attack is very likely to degrade the model performance.
Why do I get different results when I set random seeds before and after the dataset? Taking the GCN model as an example, using the CORA dataset after meta attack (ptd_rate=0.05), the model's performance differs greatly when I set random seeds before and after the dataset.
The text was updated successfully, but these errors were encountered: