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examples/graph/cgscore_experiments/attack_method/attack_dice.py

@@ -12,15 +12,15 @@ import argparse
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--seed', type=int, default=15, help='Random seed.')
-parser.add_argument('--dataset', type=str, default='Flickr', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
-parser.add_argument('--ptb_rate', type=float, default=0.25,  help='pertubation rate')
+parser.add_argument('--dataset', type=str, default='cora', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
+parser.add_argument('--ptb_rate', type=float, default=0.05,  help='pertubation rate')
 parser.add_argument('--ptb_type', type=str, default='dice', choices=['clean', 'meta', 'dice', 'minmax', 'pgd', 'random'], help='attack type')
-
+parser.add_argument('--gpu', type=int, default=0, help='GPU device ID (default: 0)')
 
 args = parser.parse_args()
 args.cuda = torch.cuda.is_available()
 print('cuda: %s' % args.cuda)
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
 
 np.random.seed(args.seed)
 torch.manual_seed(args.seed)
@@ -60,8 +60,8 @@ def test(adj):
     optimizer = optim.Adam(gcn.parameters(),
                            lr=0.01, weight_decay=5e-4)
 
-    gcn.fit(features, adj, labels, idx_train) # train without model picking
-    # gcn.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking
+    # gcn.fit(features, adj, labels, idx_train) # train without model picking
+    gcn.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking
     output = gcn.output
     loss_test = F.nll_loss(output[idx_test], labels[idx_test])
     acc_test = accuracy(output[idx_test], labels[idx_test])

examples/graph/cgscore_experiments/attack_method/attack_meta.py

@@ -23,16 +23,20 @@ parser.add_argument('--hidden', type=int, default=16,
                     help='Number of hidden units.')
 parser.add_argument('--dropout', type=float, default=0.5,
                     help='Dropout rate (1 - keep probability).')
-parser.add_argument('--dataset', type=str, default='Flickr', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
 
+parser.add_argument('--dataset', type=str, default='cora', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
 parser.add_argument('--ptb_type', type=str, default='meta', choices=['clean', 'meta', 'dice', 'minmax', 'pgd', 'random'], help='attack type')
-parser.add_argument('--ptb_rate', type=float, default=0.25,  help='pertubation rate')
+parser.add_argument('--ptb_rate', type=float, default=0.05,  help='pertubation rate')
+parser.add_argument('--gpu', type=int, default=0, help='GPU device ID (default: 0)')
+
+
 parser.add_argument('--model', type=str, default='Meta-Self',
         choices=['Meta-Self', 'A-Meta-Self', 'Meta-Train', 'A-Meta-Train'], help='model variant')
 
 args = parser.parse_args()
 
-device = torch.device("cuda:5" if torch.cuda.is_available() else "cpu")
+device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
+
 np.random.seed(args.seed)
 torch.manual_seed(args.seed)
 if device != 'cpu':
@@ -81,8 +85,8 @@ def test(adj):
               nclass=labels.max().item() + 1,
               dropout=args.dropout, device=device)
     gcn = gcn.to(device)
-    gcn.fit(features, adj, labels, idx_train) # train without model picking
-    # gcn.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking
+    # gcn.fit(features, adj, labels, idx_train) # train without model picking
+    gcn.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking
     output = gcn.output.cpu()
     loss_test = F.nll_loss(output[idx_test], labels[idx_test])
     acc_test = accuracy(output[idx_test], labels[idx_test])

examples/graph/cgscore_experiments/attack_method/attack_minmax.py

@@ -22,14 +22,16 @@ parser.add_argument('--hidden', type=int, default=16,
 parser.add_argument('--dropout', type=float, default=0.5,
                     help='Dropout rate (1 - keep probability).')
 
-parser.add_argument('--dataset', type=str, default='cora', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
+parser.add_argument('--dataset', type=str, default='Flickr', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
 parser.add_argument('--ptb_rate', type=float, default=0.25,  help='pertubation rate')
 parser.add_argument('--ptb_type', type=str, default='minmax', choices=['clean', 'meta', 'dice', 'minmax', 'pgd', 'random'], help='attack type')
 parser.add_argument('--model', type=str, default='min-max', choices=['PGD', 'min-max'], help='model variant')
+parser.add_argument('--gpu', type=int, default=0, help='GPU device ID (default: 0)')
 
 args = parser.parse_args()
 
-device = torch.device("cuda:3" if torch.cuda.is_available() else "cpu")
+device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
+
 
 np.random.seed(args.seed)
 torch.manual_seed(args.seed)
@@ -70,8 +72,8 @@ def test(adj):
               nclass=labels.max().item() + 1,
               dropout=args.dropout, device=device)
     gcn = gcn.to(device)
-    gcn.fit(features, adj, labels, idx_train) # train without model picking
-    # gcn.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking
+    # gcn.fit(features, adj, labels, idx_train) # train without model picking
+    gcn.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking
     output = gcn.output.cpu()
     loss_test = F.nll_loss(output[idx_test], labels[idx_test])
     acc_test = accuracy(output[idx_test], labels[idx_test])

examples/graph/cgscore_experiments/attack_method/attack_nettack.py

@@ -13,11 +13,11 @@ parser = argparse.ArgumentParser()
 parser.add_argument('--seed', type=int, default=15, help='Random seed.')
 parser.add_argument('--dataset', type=str, default='citeseer', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed'], help='dataset')
 
-
 args = parser.parse_args()
 args.cuda = torch.cuda.is_available()
 print('cuda: %s' % args.cuda)
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
+
 
 np.random.seed(args.seed)
 torch.manual_seed(args.seed)

examples/graph/cgscore_experiments/attack_method/attack_pgd.py

@@ -22,13 +22,13 @@ parser.add_argument('--hidden', type=int, default=16,
 parser.add_argument('--dropout', type=float, default=0.5,
                     help='Dropout rate (1 - keep probability).')
 
-parser.add_argument('--dataset', type=str, default='Flickr', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
+parser.add_argument('--dataset', type=str, default='pubmed', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
 parser.add_argument('--ptb_rate', type=float, default=0.25,  help='pertubation rate')
 parser.add_argument('--ptb_type', type=str, default='pgd', choices=['clean', 'meta', 'dice', 'minmax', 'pgd', 'random'], help='attack type')
 parser.add_argument('--model', type=str, default='PGD', choices=['PGD', 'min-max'], help='model variant')
-
+parser.add_argument('--gpu', type=int, default=0, help='GPU device ID (default: 0)')
 args = parser.parse_args()
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
 
 np.random.seed(args.seed)
 torch.manual_seed(args.seed)
@@ -39,6 +39,12 @@ if device != 'cpu':
 data = Dataset(root='/tmp/', name=args.dataset)
 adj, features, labels = data.adj, data.features, data.labels
 
+# print(features)
+# print(adj)
+print(type(features))
+print(type(adj))
+print(type(labels))
+
 features = normalize_feature(features.toarray())
 
 idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
@@ -49,7 +55,6 @@ perturbations = int(args.ptb_rate * (adj.sum()//2))
 victim_model = GCN(nfeat=features.shape[1], nclass=labels.max().item()+1, nhid=16,
         dropout=0.5, weight_decay=5e-4, device=device)
 
-
 victim_model = victim_model.to(device)
 victim_model.fit(features, adj, labels, idx_train)
 
@@ -60,16 +65,15 @@ model = model.to(device)
 
 def test(adj):
     ''' test on GCN '''
-
-
+    
     # adj = normalize_adj_tensor(adj)
     gcn = GCN(nfeat=features.shape[1],
               nhid=args.hidden,
               nclass=labels.max().item() + 1,
               dropout=args.dropout, device=device)
     gcn = gcn.to(device)
-    gcn.fit(features, adj, labels, idx_train) # train without model picking
-    # gcn.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking
+    # gcn.fit(features, adj, labels, idx_train) # train without model picking
+    gcn.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking
     output = gcn.output.cpu()
     loss_test = F.nll_loss(output[idx_test], labels[idx_test])
     acc_test = accuracy(output[idx_test], labels[idx_test])
@@ -99,6 +103,7 @@ def test_gat(adj):
 
 
 def main():
+    
     model.attack(features, adj, labels, idx_train, perturbations, epochs=args.epochs)
     print('=== testing GCN on original(clean) graph ===')
     test(adj)

examples/graph/cgscore_experiments/attack_method/attack_random.py

@@ -11,14 +11,15 @@ import os
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--seed', type=int, default=15, help='Random seed.')
-parser.add_argument('--dataset', type=str, default='cora', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
+parser.add_argument('--dataset', type=str, default='Flickr', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
 parser.add_argument('--ptb_rate', type=float, default=0.25,  help='pertubation rate')
 parser.add_argument('--ptb_type', type=str, default='random', choices=['clean', 'meta', 'dice', 'minmax', 'pgd', 'random'], help='attack type')
+parser.add_argument('--gpu', type=int, default=1, help='GPU device ID (default: 0)')
 
 args = parser.parse_args()
 args.cuda = torch.cuda.is_available()
 print('cuda: %s' % args.cuda)
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
 
 np.random.seed(args.seed)
 torch.manual_seed(args.seed)
@@ -62,8 +63,8 @@ def test(adj):
     optimizer = optim.Adam(gcn.parameters(),
                            lr=0.01, weight_decay=5e-4)
 
-    gcn.fit(features, adj, labels, idx_train, train_iters=200) # train without model picking
-    # gcn.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking
+    # gcn.fit(features, adj, labels, idx_train, train_iters=200) # train without model picking
+    gcn.fit(features, adj, labels, idx_train, idx_val, train_iters=200) # train with validation model picking
     output = gcn.output
     loss_test = F.nll_loss(output[idx_test], labels[idx_test])
     acc_test = accuracy(output[idx_test], labels[idx_test])

examples/graph/cgscore_experiments/attack_method/no_attack.py

@@ -11,7 +11,7 @@ import os
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--seed', type=int, default=15, help='Random seed.')
-parser.add_argument('--dataset', type=str, default='cora', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
+parser.add_argument('--dataset', type=str, default='Flickr', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
 parser.add_argument('--ptb_rate', type=float, default=0.00,  help='pertubation rate')
 parser.add_argument('--ptb_type', type=str, default='clean', choices=['clean', 'meta', 'dice', 'minmax', 'pgd', 'random'], help='attack type')
 

examples/graph/cgscore_experiments/attacked_adj/Flickr/clean_Flickr_0.0.pt

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examples/graph/cgscore_experiments/attacked_adj/Flickr/dice_Flickr_0.05.pt

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examples/graph/cgscore_experiments/attacked_adj/Flickr/dice_Flickr_0.25.pt

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examples/graph/cgscore_experiments/attacked_adj/Flickr/minmax_Flickr_0.05.pt

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examples/graph/cgscore_experiments/attacked_adj/citeseer/clean_citeseer_0.0.pt

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examples/graph/cgscore_experiments/attacked_adj/citeseer/meta_citeseer_0.05.pt

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examples/graph/cgscore_experiments/attacked_adj/polblogs/random_polblogs_0.25.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e4ece2e1a00b043bb514917e15dea8cfad65549bde63a9a2a1560b1b9f70ed68
+size 5974365

examples/graph/cgscore_experiments/result/GAT_pubmed_dice_0.25.csv

@@ -0,0 +1,2 @@
+dataset ,ptb_type ,ptb_rate ,accuracy
+pubmed , dice , 0.25 , 0.7693039178394828

examples/graph/cgscore_experiments/result/GAT_pubmed_minmax_0.25.csv

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+dataset ,ptb_type ,ptb_rate ,accuracy
+pubmed , minmax , 0.25 , 0.600101432737416

examples/graph/cgscore_experiments/result/GCN_pubmed_clean_0.0.csv

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+dataset ,ptb_type ,ptb_rate ,accuracy
+pubmed , clean , 0.0 , 0.8602130087485736

examples/graph/cgscore_experiments/result/GCN_pubmed_dice_0.25.csv

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+dataset ,ptb_type ,ptb_rate ,accuracy
+pubmed , dice , 0.25 , 0.8168505135032331

examples/graph/cgscore_experiments/result/GNNGuard_Flickr_minmax_0.25.csv

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+dataset ,ptb_type ,ptb_rate ,accuracy
+Flickr , minmax , 0.25 , 0.7496699669966996

examples/graph/cgscore_experiments/result/GNNGuard_citeseer_dice_0.25.csv

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+dataset ,ptb_type ,ptb_rate ,accuracy
+citeseer , dice , 0.25 , 0.6901658767772513

examples/graph/cgscore_experiments/result/GNNGuard_citeseer_minmax_0.25.csv

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+dataset ,ptb_type ,ptb_rate ,accuracy
+citeseer , minmax , 0.25 , 0.6860189573459716

examples/graph/cgscore_experiments/result/GNNGuard_cora_meta_0.25.csv

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+dataset ,ptb_type ,ptb_rate ,accuracy
+cora , meta , 0.25 , 0.7077464788732395

examples/graph/cgscore_experiments/result/GNNGuard_polblogs_clean_0.0.csv

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+dataset ,ptb_type ,ptb_rate ,accuracy
+polblogs , clean , 0.0 , 0.4877300613496933
+polblogs , clean , 0.0 , 0.4877300613496933

examples/graph/cgscore_experiments/result/GNNGuard_polblogs_dice_0.25.csv

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+dataset ,ptb_type ,ptb_rate ,accuracy
+polblogs , dice , 0.25 , 0.4877300613496933
+polblogs , dice , 0.25 , 0.4877300613496933

examples/graph/cgscore_experiments/result/GNNGuard_pubmed_clean_0.0.csv

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+dataset ,ptb_type ,ptb_rate ,accuracy
+pubmed , clean , 0.0 , 0.8444909344490935