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Thank you for the wonderful work!
I use default setting for the node-level experiment on Cora.
python run_node_exps.py --model GCN\ --dataset Cora\ --is_iid iid\ --num_workers 5\ --num_mali 1\ --epoch_backdoor 0\ --trigger_size 3\ --trigger_type renyi\ --trigger_position random\ --poisoning_intensity 0.1\ --overlapping_rate 0.0
The result is:
The variance on average_ASR is really huge. This variance even increased when I try Cora with GraphSage:
# in run_node_exps.py # seeds = rs.randint(1000, size=5) seeds = [996, 996, 996, 996, 996]
And the result looks good:
Then, I independently fix the seed for these three lines in backdoor_node_clf.py:
def main(args, logger): # np.random.seed(args.seed) # torch.manual_seed(args.seed) # torch.cuda.manual_seed(args.seed) np.random.seed(996) torch.manual_seed(996) torch.cuda.manual_seed(996)
However, the result is:
Finally, I thought it may be due to the small size of Cora so I try Pubmed:
Is this huge variation on average_ASR normal, just with different seed? What could be the possible reasons? Thank you.
The text was updated successfully, but these errors were encountered:
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Thank you for the wonderful work!
I use default setting for the node-level experiment on Cora.
python run_node_exps.py --model GCN\ --dataset Cora\ --is_iid iid\ --num_workers 5\ --num_mali 1\ --epoch_backdoor 0\ --trigger_size 3\ --trigger_type renyi\ --trigger_position random\ --poisoning_intensity 0.1\ --overlapping_rate 0.0
The result is:
The variance on average_ASR is really huge.
This variance even increased when I try Cora with GraphSage:
And the result looks good:
Then, I independently fix the seed for these three lines in backdoor_node_clf.py:
However, the result is:
Finally, I thought it may be due to the small size of Cora so I try Pubmed:
Is this huge variation on average_ASR normal, just with different seed? What could be the possible reasons? Thank you.
The text was updated successfully, but these errors were encountered: