Add files using upload-large-folder tool

docs/make.bat

@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

docs/source/deeprobust.graph.data.rst

@@ -0,0 +1,24 @@
+deeprobust.graph.data package
+=============================
+
+Submodules
+----------
+
+deeprobust.graph.data.attacked\_data module
+-------------------------------------------
+
+.. automodule:: deeprobust.graph.data.attacked_data
+   :members:
+
+deeprobust.graph.data.dataset module
+------------------------------------
+
+.. automodule:: deeprobust.graph.data.dataset
+   :members:
+
+
+Module contents
+---------------
+
+.. automodule:: deeprobust.graph.data
+   :members:

docs/source/deeprobust.graph.rl.rst

@@ -0,0 +1,66 @@
+deeprobust.graph.rl package
+===========================
+
+Submodules
+----------
+
+deeprobust.graph.rl.env module
+------------------------------
+
+.. automodule:: deeprobust.graph.rl.env
+   :members:
+
+deeprobust.graph.rl.nipa\_config module
+---------------------------------------
+
+.. automodule:: deeprobust.graph.rl.nipa_config
+   :members:
+
+deeprobust.graph.rl.nipa\_env module
+------------------------------------
+
+.. automodule:: deeprobust.graph.rl.nipa_env
+   :members:
+
+deeprobust.graph.rl.nipa\_nstep\_replay\_mem module
+---------------------------------------------------
+
+.. automodule:: deeprobust.graph.rl.nipa_nstep_replay_mem
+   :members:
+
+deeprobust.graph.rl.nipa\_q\_net\_node module
+---------------------------------------------
+
+.. automodule:: deeprobust.graph.rl.nipa_q_net_node
+   :members:
+
+deeprobust.graph.rl.nstep\_replay\_mem module
+---------------------------------------------
+
+.. automodule:: deeprobust.graph.rl.nstep_replay_mem
+   :members:
+
+deeprobust.graph.rl.q\_net\_node module
+---------------------------------------
+
+.. automodule:: deeprobust.graph.rl.q_net_node
+   :members:
+
+deeprobust.graph.rl.rl\_s2v\_config module
+------------------------------------------
+
+.. automodule:: deeprobust.graph.rl.rl_s2v_config
+   :members:
+
+deeprobust.graph.rl.rl\_s2v\_env module
+---------------------------------------
+
+.. automodule:: deeprobust.graph.rl.rl_s2v_env
+   :members:
+
+
+Module contents
+---------------
+
+.. automodule:: deeprobust.graph.rl
+   :members:

docs/source/deeprobust.graph.targeted_attack.rst

@@ -0,0 +1,54 @@
+deeprobust.graph.targeted\_attack package
+=========================================
+
+Submodules
+----------
+
+deeprobust.graph.targeted\_attack.base\_attack module
+-----------------------------------------------------
+
+.. automodule:: deeprobust.graph.targeted_attack.base_attack
+   :members:
+
+deeprobust.graph.targeted\_attack.fga module
+--------------------------------------------
+
+.. automodule:: deeprobust.graph.targeted_attack.fga
+   :members:
+
+deeprobust.graph.targeted\_attack.ig\_attack module
+---------------------------------------------------
+
+.. automodule:: deeprobust.graph.targeted_attack.ig_attack
+   :members:
+
+deeprobust.graph.targeted\_attack.nettack module
+------------------------------------------------
+
+.. automodule:: deeprobust.graph.targeted_attack.nettack
+   :members:
+
+deeprobust.graph.targeted\_attack.rl\_s2v module
+------------------------------------------------
+
+.. automodule:: deeprobust.graph.targeted_attack.rl_s2v
+   :members:
+
+deeprobust.graph.targeted\_attack.rnd module
+--------------------------------------------
+
+.. automodule:: deeprobust.graph.targeted_attack.rnd
+   :members:
+
+
+deeprobust.graph.targeted\_attack.sga module
+--------------------------------------------
+
+.. automodule:: deeprobust.graph.targeted_attack.sga
+   :members:
+
+Module contents
+---------------
+
+.. automodule:: deeprobust.graph.targeted_attack
+   :members:

docs/source/deeprobust.image.attack.rst

@@ -0,0 +1,84 @@
+deeprobust.image.attack package
+===============================
+
+Submodules
+----------
+
+deeprobust.image.attack.BPDA module
+-----------------------------------
+
+.. automodule:: deeprobust.image.attack.BPDA
+   :members:
+
+deeprobust.image.attack.Nattack module
+--------------------------------------
+
+.. automodule:: deeprobust.image.attack.Nattack
+   :members:
+
+deeprobust.image.attack.Universal module
+----------------------------------------
+
+.. automodule:: deeprobust.image.attack.Universal
+   :members:
+
+deeprobust.image.attack.YOPOpgd module
+--------------------------------------
+
+.. automodule:: deeprobust.image.attack.YOPOpgd
+   :members:
+
+deeprobust.image.attack.base\_attack module
+-------------------------------------------
+
+.. automodule:: deeprobust.image.attack.base_attack
+   :members:
+
+deeprobust.image.attack.cw module
+---------------------------------
+
+.. automodule:: deeprobust.image.attack.cw
+   :members:
+
+deeprobust.image.attack.deepfool module
+---------------------------------------
+
+.. automodule:: deeprobust.image.attack.deepfool
+   :members:
+
+deeprobust.image.attack.fgsm module
+-----------------------------------
+
+.. automodule:: deeprobust.image.attack.fgsm
+   :members:
+
+deeprobust.image.attack.l2\_attack module
+-----------------------------------------
+
+.. automodule:: deeprobust.image.attack.l2_attack
+   :members:
+
+deeprobust.image.attack.lbfgs module
+------------------------------------
+
+.. automodule:: deeprobust.image.attack.lbfgs
+   :members:
+
+deeprobust.image.attack.onepixel module
+---------------------------------------
+
+.. automodule:: deeprobust.image.attack.onepixel
+   :members:
+
+deeprobust.image.attack.pgd module
+----------------------------------
+
+.. automodule:: deeprobust.image.attack.pgd
+   :members:
+
+
+Module contents
+---------------
+
+.. automodule:: deeprobust.image.attack
+   :members:

docs/source/deeprobust.image.defense.rst

@@ -0,0 +1,60 @@
+deeprobust.image.defense package
+================================
+
+Submodules
+----------
+
+deeprobust.image.defense.LIDclassifier module
+---------------------------------------------
+
+.. automodule:: deeprobust.image.defense.LIDclassifier
+   :members:
+
+deeprobust.image.defense.TherEncoding module
+--------------------------------------------
+
+.. automodule:: deeprobust.image.defense.TherEncoding
+   :members:
+
+deeprobust.image.defense.YOPO module
+------------------------------------
+
+.. automodule:: deeprobust.image.defense.YOPO
+   :members:
+
+deeprobust.image.defense.base\_defense module
+---------------------------------------------
+
+.. automodule:: deeprobust.image.defense.base_defense
+   :members:
+
+deeprobust.image.defense.fast module
+------------------------------------
+
+.. automodule:: deeprobust.image.defense.fast
+   :members:
+
+deeprobust.image.defense.fgsmtraining module
+--------------------------------------------
+
+.. automodule:: deeprobust.image.defense.fgsmtraining
+   :members:
+
+deeprobust.image.defense.pgdtraining module
+-------------------------------------------
+
+.. automodule:: deeprobust.image.defense.pgdtraining
+   :members:
+
+deeprobust.image.defense.trades module
+--------------------------------------
+
+.. automodule:: deeprobust.image.defense.trades
+   :members:
+
+
+Module contents
+---------------
+
+.. automodule:: deeprobust.image.defense
+   :members:

docs/source/deeprobust.image.netmodels.rst

@@ -0,0 +1,66 @@
+deeprobust.image.netmodels package
+==================================
+
+Submodules
+----------
+
+deeprobust.image.netmodels.CNN module
+-------------------------------------
+
+.. automodule:: deeprobust.image.netmodels.CNN
+   :members:
+
+deeprobust.image.netmodels.CNN\_multilayer module
+-------------------------------------------------
+
+.. automodule:: deeprobust.image.netmodels.CNN_multilayer
+   :members:
+
+deeprobust.image.netmodels.YOPOCNN module
+-----------------------------------------
+
+.. automodule:: deeprobust.image.netmodels.YOPOCNN
+   :members:
+
+deeprobust.image.netmodels.densenet module
+------------------------------------------
+
+.. automodule:: deeprobust.image.netmodels.densenet
+   :members:
+
+deeprobust.image.netmodels.preact\_resnet module
+------------------------------------------------
+
+.. automodule:: deeprobust.image.netmodels.preact_resnet
+   :members:
+
+deeprobust.image.netmodels.resnet module
+----------------------------------------
+
+.. automodule:: deeprobust.image.netmodels.resnet
+   :members:
+
+deeprobust.image.netmodels.train\_model module
+----------------------------------------------
+
+.. automodule:: deeprobust.image.netmodels.train_model
+   :members:
+
+deeprobust.image.netmodels.train\_resnet module
+-----------------------------------------------
+
+.. automodule:: deeprobust.image.netmodels.train_resnet
+   :members:
+
+deeprobust.image.netmodels.vgg module
+-------------------------------------
+
+.. automodule:: deeprobust.image.netmodels.vgg
+   :members:
+
+
+Module contents
+---------------
+
+.. automodule:: deeprobust.image.netmodels
+   :members:

docs/source/deeprobust.image.rst

@@ -0,0 +1,45 @@
+deeprobust.image package
+========================
+
+Subpackages
+-----------
+
+.. toctree::
+
+   deeprobust.image.attack
+   deeprobust.image.defense
+   deeprobust.image.netmodels
+
+Submodules
+----------
+
+deeprobust.image.config module
+------------------------------
+
+.. automodule:: deeprobust.image.config
+   :members:
+
+deeprobust.image.evaluation\_attack module
+------------------------------------------
+
+.. automodule:: deeprobust.image.evaluation_attack
+   :members:
+
+deeprobust.image.optimizer module
+---------------------------------
+
+.. automodule:: deeprobust.image.optimizer
+   :members:
+
+deeprobust.image.utils module
+-----------------------------
+
+.. automodule:: deeprobust.image.utils
+   :members:
+
+
+Module contents
+---------------
+
+.. automodule:: deeprobust.image
+   :members:

docs/source/modules.rst

@@ -0,0 +1,7 @@
+deeprobust
+==========
+
+.. toctree::
+   :maxdepth: 4
+
+   deeprobust

examples/graph/cgscore_experiments/attack_method/attack_dice.py

@@ -0,0 +1,90 @@
+import torch
+import numpy as np
+import torch.nn.functional as F
+import torch.optim as optim
+from deeprobust.graph.defense import GCN
+from deeprobust.graph.global_attack import DICE
+from deeprobust.graph.utils import *
+from deeprobust.graph.data import Dataset
+import os
+
+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('--ptb_type', type=str, default='dice', choices=['clean', 'meta', 'dice', 'minmax', 'pgd', 'random'], help='attack type')
+
+
+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")
+
+np.random.seed(args.seed)
+torch.manual_seed(args.seed)
+if args.cuda:
+    torch.cuda.manual_seed(args.seed)
+
+# data = Dataset(root='/tmp/', name=args.dataset, setting='prognn')
+data = Dataset(root='/tmp/', name=args.dataset)
+adj, features, labels = data.adj, data.features, data.labels
+idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
+idx_unlabeled = np.union1d(idx_val, idx_test)
+
+# Setup Attack Model
+model = DICE()
+
+n_perturbations = int(args.ptb_rate * (adj.sum()//2))
+
+model.attack(adj, labels, n_perturbations)
+modified_adj = model.modified_adj
+
+adj, features, labels = preprocess(adj, features, labels, preprocess_adj=False, sparse=True, device=device)
+
+modified_adj = normalize_adj(modified_adj)
+modified_adj = sparse_mx_to_torch_sparse_tensor(modified_adj)
+modified_adj = modified_adj.to(device)
+
+def test(adj):
+    ''' test on GCN '''
+    # adj = normalize_adj_tensor(adj)
+    gcn = GCN(nfeat=features.shape[1],
+              nhid=16,
+              nclass=labels.max().item() + 1,
+              dropout=0.5, device=device)
+
+    gcn = gcn.to(device)
+
+    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
+    output = gcn.output
+    loss_test = F.nll_loss(output[idx_test], labels[idx_test])
+    acc_test = accuracy(output[idx_test], labels[idx_test])
+    print("Test set results:",
+          "loss= {:.4f}".format(loss_test.item()),
+          "accuracy= {:.4f}".format(acc_test.item()))
+
+    return acc_test.item()
+
+def main():
+    print('=== testing GCN on original(clean) graph ===')
+    test(adj)
+    
+    save_dir = f"../attacked_adj/{args.dataset}"
+    os.makedirs(save_dir, exist_ok=True)  
+
+    modified_adj_save = modified_adj.to_dense()
+    save_path = os.path.join(save_dir, f"{args.ptb_type}_{args.dataset}_{args.ptb_rate}.pt")
+    torch.save(modified_adj_save, save_path)
+    
+    print('=== testing GCN on perturbed graph ===')
+    test(modified_adj)
+    
+if __name__ == '__main__':
+    main()
+

examples/graph/cgscore_experiments/attack_method/attack_meta.py

@@ -0,0 +1,118 @@
+import torch
+import numpy as np
+import torch.nn.functional as F
+import torch.optim as optim
+from deeprobust.graph.defense import GCN
+from deeprobust.graph.global_attack import MetaApprox, Metattack
+from deeprobust.graph.utils import *
+from deeprobust.graph.data import Dataset
+import argparse
+import os
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--no-cuda', action='store_true', default=False,
+                    help='Disables CUDA training.')
+parser.add_argument('--seed', type=int, default=15, help='Random seed.')
+parser.add_argument('--epochs', type=int, default=200,
+                    help='Number of epochs to train.')
+parser.add_argument('--lr', type=float, default=0.01,
+                    help='Initial learning rate.')
+parser.add_argument('--weight_decay', type=float, default=5e-4,
+                    help='Weight decay (L2 loss on parameters).')
+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('--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('--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")
+np.random.seed(args.seed)
+torch.manual_seed(args.seed)
+if device != 'cpu':
+    torch.cuda.manual_seed(args.seed)
+
+
+# cora, citeseer, polglogs, pubmed, flickr
+# data = Dataset(root='/tmp/', name=args.dataset, setting='prognn')
+data = Dataset(root='/tmp/', name=args.dataset)
+adj, features, labels = data.adj, data.features, data.labels
+idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
+idx_unlabeled = np.union1d(idx_val, idx_test)
+
+perturbations = int(args.ptb_rate * (adj.sum()//2))
+adj, features, labels = preprocess(adj, features, labels, preprocess_adj=False)
+
+# Setup Surrogate Model
+surrogate = GCN(nfeat=features.shape[1], nclass=labels.max().item()+1, nhid=16,
+        dropout=0.5, with_relu=False, with_bias=True, weight_decay=5e-4, device=device)
+
+surrogate = surrogate.to(device)
+surrogate.fit(features, adj, labels, idx_train)
+
+# Setup Attack Model
+if 'Self' in args.model:
+    lambda_ = 0
+if 'Train' in args.model:
+    lambda_ = 1
+if 'Both' in args.model:
+    lambda_ = 0.5
+
+if 'A' in args.model:
+    model = MetaApprox(model=surrogate, nnodes=adj.shape[0], feature_shape=features.shape, attack_structure=True, attack_features=False, device=device, lambda_=lambda_)
+
+else:
+    model = Metattack(model=surrogate, nnodes=adj.shape[0], feature_shape=features.shape,  attack_structure=True, attack_features=False, device=device, lambda_=lambda_)
+
+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
+    output = gcn.output.cpu()
+    loss_test = F.nll_loss(output[idx_test], labels[idx_test])
+    acc_test = accuracy(output[idx_test], labels[idx_test])
+    print("Test set results:",
+          "loss= {:.4f}".format(loss_test.item()),
+          "accuracy= {:.4f}".format(acc_test.item()))
+
+    return acc_test.item()
+
+
+def main():
+    
+    model.attack(features, adj, labels, idx_train, idx_unlabeled, perturbations, ll_constraint=False)
+    print('=== testing GCN on original(clean) graph ===')
+    test(adj)
+    modified_adj = model.modified_adj
+    
+    save_dir = f"../attacked_adj/{args.dataset}"
+    os.makedirs(save_dir, exist_ok=True)  
+
+    save_path = os.path.join(save_dir, f"{args.ptb_type}_{args.dataset}_{args.ptb_rate}.pt")
+    torch.save(modified_adj, save_path)
+    
+    # modified_features = model.modified_features
+    test(modified_adj)
+
+    # # if you want to save the modified adj/features, uncomment the code below
+    # model.save_adj(root='./', name=f'mod_adj')
+    # model.save_features(root='./', name='mod_features')
+
+if __name__ == '__main__':
+    main()
+

examples/graph/cgscore_experiments/attack_method/attack_pgd.py

@@ -0,0 +1,124 @@
+import torch
+import numpy as np
+import torch.nn.functional as F
+import torch.optim as optim
+from deeprobust.graph.defense import GCN, GAT
+from deeprobust.graph.global_attack import MinMax, PGDAttack
+from deeprobust.graph.utils import *
+from deeprobust.graph.data import Dataset, Dpr2Pyg
+import argparse
+import os
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--seed', type=int, default=15, help='Random seed.')
+parser.add_argument('--epochs', type=int, default=25,
+                    help='Number of epochs to train.')
+parser.add_argument('--lr', type=float, default=0.01,
+                    help='Initial learning rate.')
+parser.add_argument('--weight_decay', type=float, default=5e-4,
+                    help='Weight decay (L2 loss on parameters).')
+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('--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')
+
+args = parser.parse_args()
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+np.random.seed(args.seed)
+torch.manual_seed(args.seed)
+if device != 'cpu':
+    torch.cuda.manual_seed(args.seed)
+
+# data = Dataset(root='/tmp/', name=args.dataset, setting='prognn')
+data = Dataset(root='/tmp/', name=args.dataset)
+adj, features, labels = data.adj, data.features, data.labels
+
+features = normalize_feature(features.toarray())
+
+idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
+adj, features, labels = preprocess(adj, features, labels, preprocess_adj=False)
+
+perturbations = int(args.ptb_rate * (adj.sum()//2))
+# Setup Victim Model
+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)
+
+# Setup Attack Model
+model = PGDAttack(model=victim_model, nnodes=adj.shape[0], loss_type='CE', device=device)
+
+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
+    output = gcn.output.cpu()
+    loss_test = F.nll_loss(output[idx_test], labels[idx_test])
+    acc_test = accuracy(output[idx_test], labels[idx_test])
+    print("Test set results:",
+          "loss= {:.4f}".format(loss_test.item()),
+          "accuracy= {:.4f}".format(acc_test.item()))
+
+    return acc_test.item()
+
+def test_gat(adj):
+    
+    perturbed_adj = sp.csr_matrix(adj.cpu().numpy())
+    gat = GAT(nfeat=features.shape[1],
+        nhid=8, heads=8,
+        nclass=labels.max().item() + 1,
+        dropout=0.5, device=device)
+    gat = gat.to(device)
+
+    # test on clean graph
+    print('==================')
+    print('=== train on clean graph ===')
+
+    pyg_data = Dpr2Pyg(data)
+    pyg_data.update_edge_index(perturbed_adj) # inplace operation
+    gat.fit(pyg_data, train_iters=200, verbose=True) # train with earlystopping
+    gat.test()
+
+
+def main():
+    model.attack(features, adj, labels, idx_train, perturbations, epochs=args.epochs)
+    print('=== testing GCN on original(clean) graph ===')
+    test(adj)
+    modified_adj = model.modified_adj
+    # modified_features = model.modified_features
+    print(modified_adj)
+    print(type(modified_adj))
+    save_dir = f"../attacked_adj/{args.dataset}"
+    os.makedirs(save_dir, exist_ok=True)  
+
+    save_path = os.path.join(save_dir, f"{args.ptb_type}_{args.dataset}_{args.ptb_rate}.pt")
+    torch.save(modified_adj, save_path)
+    
+    test(modified_adj)
+    test_gat(modified_adj)
+
+    # # if you want to save the modified adj/features, uncomment the code below
+    # model.save_adj(root='./', name=f'mod_adj')
+    # model.save_features(root='./', name='mod_features')
+
+if __name__ == '__main__':
+    main()
+

examples/graph/cgscore_experiments/attack_method/attack_random.py

@@ -0,0 +1,92 @@
+import torch
+import numpy as np
+import torch.nn.functional as F
+import torch.optim as optim
+from deeprobust.graph.defense import GCN
+from deeprobust.graph.global_attack import Random
+from deeprobust.graph.utils import *
+from deeprobust.graph.data import Dataset
+import argparse
+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('--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')
+
+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")
+
+np.random.seed(args.seed)
+torch.manual_seed(args.seed)
+if args.cuda:
+    torch.cuda.manual_seed(args.seed)
+    
+# data = Dataset(root='/tmp/', name=args.dataset, setting='prognn')
+data = Dataset(root='/tmp/', name=args.dataset)
+adj, features, labels = data.adj, data.features, data.labels
+idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
+idx_unlabeled = np.union1d(idx_val, idx_test)
+
+# Setup Attack Model
+model = Random()
+
+n_perturbations = int(args.ptb_rate * (adj.sum()//2))
+
+model.attack(adj, n_perturbations, type='flip') # type ['add', 'remove', 'flip']
+modified_adj = model.modified_adj
+
+adj, features, labels = preprocess(adj, features, labels, preprocess_adj=False, sparse=True)
+adj = adj.to(device)
+features = features.to(device)
+labels = labels.to(device)
+
+modified_adj = normalize_adj(modified_adj)
+modified_adj = sparse_mx_to_torch_sparse_tensor(modified_adj)
+modified_adj = modified_adj.to(device)
+
+
+def test(adj):
+    ''' test on GCN '''
+    # adj = normalize_adj_tensor(adj)
+    gcn = GCN(nfeat=features.shape[1],
+              nhid=16,
+              nclass=labels.max().item() + 1,
+              dropout=0.5, device=device)
+
+    gcn = gcn.to(device)
+
+    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
+    output = gcn.output
+    loss_test = F.nll_loss(output[idx_test], labels[idx_test])
+    acc_test = accuracy(output[idx_test], labels[idx_test])
+    print("Test set results:",
+          "loss= {:.4f}".format(loss_test.item()),
+          "accuracy= {:.4f}".format(acc_test.item()))
+
+    return acc_test.item()
+
+def main():
+    print('=== testing GCN on original(clean) graph ===')
+    test(adj)
+    save_dir = f"../attacked_adj/{args.dataset}"
+    os.makedirs(save_dir, exist_ok=True)  
+
+    modified_adj_save = modified_adj.to_dense()
+    save_path = os.path.join(save_dir, f"{args.ptb_type}_{args.dataset}_{args.ptb_rate}.pt")
+    torch.save(modified_adj_save, save_path)
+    
+    print('=== testing GCN on perturbed graph ===')
+
+    test(modified_adj)
+
+if __name__ == '__main__':
+    main()
+

examples/graph/cgscore_experiments/attack_method/no_attack.py

@@ -0,0 +1,78 @@
+import torch
+import numpy as np
+import torch.nn.functional as F
+import torch.optim as optim
+from deeprobust.graph.defense import GCN
+from deeprobust.graph.global_attack import Random
+from deeprobust.graph.utils import *
+from deeprobust.graph.data import Dataset
+import argparse
+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('--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')
+
+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")
+
+np.random.seed(args.seed)
+torch.manual_seed(args.seed)
+if args.cuda:
+    torch.cuda.manual_seed(args.seed)
+
+# data = Dataset(root='/tmp/', name=args.dataset, setting='prognn')
+data = Dataset(root='/tmp/', name=args.dataset)
+adj, features, labels = data.adj, data.features, data.labels
+idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
+idx_unlabeled = np.union1d(idx_val, idx_test)
+
+
+adj, features, labels = preprocess(adj, features, labels, preprocess_adj=False, sparse=True)
+adj, features, labels = adj.to(device), features.to(device), labels.to(device)
+adj = adj.to_dense()
+
+def test(adj):
+    ''' test on GCN '''
+    # adj = normalize_adj_tensor(adj)
+    gcn = GCN(nfeat=features.shape[1],
+              nhid=16,
+              nclass=labels.max().item() + 1,
+              dropout=0.5, device=device)
+
+    gcn = gcn.to(device)
+
+    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
+    output = gcn.output
+    loss_test = F.nll_loss(output[idx_test], labels[idx_test])
+    acc_test = accuracy(output[idx_test], labels[idx_test])
+    print("Test set results:",
+          "loss= {:.4f}".format(loss_test.item()),
+          "accuracy= {:.4f}".format(acc_test.item()))
+
+    return acc_test.item()
+
+def main():
+    
+
+    save_dir = f"../attacked_adj/{args.dataset}"
+    os.makedirs(save_dir, exist_ok=True)  
+
+    save_path = os.path.join(save_dir, f"{args.ptb_type}_{args.dataset}_{args.ptb_rate}.pt")
+    torch.save(adj, save_path)
+    
+    print('=== testing GCN on original(clean) graph ===')
+    test(adj)
+    
+
+if __name__ == '__main__':
+    main()
+