torch.FloatTensor
output (log probabilities) of SGC
"""
self.eval()
self.dropout = dropout
if pyg_data == None:
data = self.data
else:
data = pyg_data[0].to(self.device)
self.data = data
return self.forward(data)
if __name__ == "__main__":
from deeprobust.graph.data import Dataset, Dpr2Pyg
# from deeprobust.graph.defense import SGC
data = Dataset(root='/tmp/', name='cora')
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
sgc = SGC(nfeat=features.shape[1],
nclass=labels.max().item() + 1,
device='cpu')
sgc = sgc.to('cpu')
pyg_data = Dpr2Pyg(data)
sgc.fit(pyg_data, verbose=True) # train with earlystopping
sgc.test()
print(sgc.predict())
data = Dataset(root='/tmp/', name=args.dataset, setting='nettack', seed=15)
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
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 ===')
data1 = Dpr2Pyg(data)
gat.fit(data1, verbose=True) # train with earlystopping
gat.test()
print('==================')
print('=== train on clean graph + diffusion===')
pyg_data = Dpr2Pyg(data)
data2 = pyg_data[0]
gdc = T.GDC(self_loop_weight=1, normalization_in='sym',
normalization_out='col',
diffusion_kwargs=dict(method='ppr', alpha=0.05),
sparsification_kwargs=dict(method='topk', k=128,
dim=0), exact=True)
data2 = gdc(data2)