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README.rst

Run node feature attack experiments

Here we list the commands to reproduce the results for node feature attack experiments. Each of the experiment in run_node.py loops over seed = 0, 1, 2, 3, 4. After running the experiments with the model saved under GIB/results/, use the script experiments/GIB_node_analysis.ipynb (Section 3) to perform node feature evasive attacks and obtain the results.

Cora

Cora with GIB-Cat:

python run_node.py --exp_id=node1.0 --data_type=Cora --model_type=GAT --beta1=0.01 --beta2=0.01 --struct_dropout_mode='\("DNsampling","multi-categorical-sum",0.1,2,2\)' --gpuid=0

Cora with AIB-Cat:

python run_node.py --exp_id=node1.0 --data_type=Cora --model_type=GAT --beta1=-1 --beta2=0.01 --struct_dropout_mode='\("DNsampling","multi-categorical-sum",0.1,2,2\)' --gpuid=0

Cora with GIB-Bern:

python run_node.py --exp_id=node1.0 --data_type=Cora --model_type=GAT --beta1=0.001 --beta2=0.01 --struct_dropout_mode='\("DNsampling","Bernoulli",0.05,0.5,"norm",2\)' --gpuid=0

Cora with AIB-Bern:

python run_node.py --exp_id=node1.0 --data_type=Cora --model_type=GAT --beta1=-1 --beta2=0.01 --struct_dropout_mode='\("DNsampling","Bernoulli",0.05,0.5,"norm",2\)' --gpuid=0

Cora with GAT:

python run_node.py --exp_id=node1.0 --data_type=Cora --model_type=GAT --beta1=-1 --beta2=-1 --struct_dropout_mode='\("standard",0.6\)' --gpuid=0

Cora with GCN:

python run_node.py --exp_id=node1.0 --data_type=Cora --model_type=GCN --beta1=-1 --beta2=-1 --gpuid=0

Cora with GCNJaccard:

python run_node.py --exp_id=node1.0 --data_type=Cora --model_type=GCNJaccard --beta1=-1 --beta2=-1 --latent_size=16 --lr=1e-2 --weight_decay=5e-4 --threshold=0.05 --gpuid=0

Cora with RGCN:

python run_node.py --exp_id=node1.0 --data_type=Cora --model_type=RGCN --beta1=5e-4 --beta2=-1 --latent_size=64 --lr=1e-2 --weight_decay=5e-4 --gamma=0.3 --gpuid=0

Pubmed

Pubmed with GIB-Cat:

python run_node.py --exp_id=node1.0 --data_type=Pubmed --model_type=GAT --beta1=0.001 --beta2=0.01 --struct_dropout_mode='\("DNsampling","multi-categorical-sum",1,3,2\)' --gpuid=0

Pubmed with AIB-Cat:

python run_node.py --exp_id=node1.0 --data_type=Pubmed --model_type=GAT --beta1=-1 --beta2=0.01 --struct_dropout_mode='\("DNsampling","multi-categorical-sum",1,3,2\)' --gpuid=0

Pubmed with GIB-Bern:

python run_node.py --exp_id=node1.0 --data_type=Pubmed --model_type=GAT --beta1=0.01 --beta2=0.01 --struct_dropout_mode='\("Nsampling","Bernoulli",0.05,0.5,"norm"\)' --gpuid=0

Pubmed with AIB-Bern:

python run_node.py --exp_id=node1.0 --data_type=Pubmed --model_type=GAT --beta1=-1 --beta2=0.01 --struct_dropout_mode='\("Nsampling","Bernoulli",0.05,0.5,"norm"\)' --gpuid=0

Pubmed with GAT:

python run_node.py --exp_id=node1.0 --data_type=Pubmed --model_type=GAT --beta1=-1 --beta2=-1 --struct_dropout_mode='\("standard",0.6\)' --gpuid=0

Pubmed with GCN:

python run_node.py --exp_id=node1.0 --data_type=Pubmed --model_type=GCN --beta1=-1 --beta2=-1 --gpuid=0

Pubmed with GCNJaccard:

python run_node.py --exp_id=node1.0 --data_type=Pubmed --model_type=GCNJaccard --beta1=-1 --beta2=-1 --latent_size=16 --lr=1e-2 --weight_decay=5e-4 --threshold=0.05 --gpuid=0

Pubmed with RGCN:

python run_node.py --exp_id=node1.0 --data_type=Pubmed --model_type=RGCN --beta1=5e-4 --beta2=-1 --latent_size=16 --lr=1e-2 --weight_decay=5e-4 --gamma=0.1 --gpuid=0

Citeseer

Citeseer with GIB-Cat:

python run_node.py --exp_id=node1.0 --data_type=citeseer --model_type=GAT --beta1=0.001 --beta2=0.01 --struct_dropout_mode='\("DNsampling","multi-categorical-sum",0.1,2,2\)' --gpuid=0

Citeseer with AIB-Cat:

python run_node.py --exp_id=node1.0 --data_type=citeseer --model_type=GAT --beta1=-1 --beta2=0.01 --struct_dropout_mode='\("DNsampling","multi-categorical-sum",0.1,2,2\)' --gpuid=0

Citeseer with GIB-Bern:

python run_node.py --exp_id=node1.0 --data_type=citeseer --model_type=GAT --beta1=0.1 --beta2=0.01 --struct_dropout_mode='\("DNsampling","Bernoulli",0.05,0.5,"norm",2\)' --gpuid=0

Citeseer with AIB-Bern:

python run_node.py --exp_id=node1.0 --data_type=citeseer --model_type=GAT --beta1=-1 --beta2=0.01 --struct_dropout_mode='\("DNsampling","Bernoulli",0.05,0.5,"norm",2\)' --gpuid=0

Citeseer with GAT:

python run_node.py --exp_id=node1.0 --data_type=citeseer --model_type=GAT --beta1=-1 --beta2=-1 --struct_dropout_mode='\("standard",0.6\)' --gpuid=0

Citeseer with GCN:

python run_node.py --exp_id=node1.0 --data_type=citeseer --model_type=GCN --beta1=-1 --beta2=-1 --gpuid=0

Citeseer with GCNJaccard:

python run_node.py --exp_id=node1.0 --data_type=citeseer --model_type=GCNJaccard --beta1=-1 --beta2=-1 --latent_size=16 --lr=1e-2 --weight_decay=5e-4 --threshold=0.05 --gpuid=0

Citeseer with RGCN:

python run_node.py --exp_id=node1.0 --data_type=citeseer --model_type=RGCN --beta1=5e-4 --beta2=-1 --latent_size=64 --lr=1e-2 --weight_decay=5e-4 --gamma=0.3 --gpuid=0