def create_masked_gcn_model(data_name, nhid=16, dropout=0.5,
lr=0.02, weight_decay=5e-4):
dataset = get_planetoid_dataset(data_name, True)
model = MaskedGCN(dataset, nhid, dropout)
optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
return dataset, model, optimizer
def create_gat_model(data_name,
nhid=8,
first_heads=8,
output_heads=1,
dropout=0.6,
lr=0.005,
weight_decay=5e-4):
dataset = get_planetoid_dataset(data_name, True)
model = GAT(dataset, nhid, first_heads, output_heads, dropout)
optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
return dataset, model, optimizer
super(Net, self).__init__()
self.conv1 = ARMAConv(dataset.num_features,
args.hidden,
args.num_stacks,
args.num_layers,
args.shared_weights,
dropout=args.skip_dropout)
self.conv2 = ARMAConv(args.hidden,
dataset.num_classes,
args.num_stacks,
args.num_layers,
args.shared_weights,
dropout=args.skip_dropout)
def reset_parameters(self):
self.conv1.reset_parameters()
self.conv2.reset_parameters()
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = F.relu(self.conv1(x, edge_index))
x = F.dropout(x, p=args.dropout, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
dataset = get_planetoid_dataset(args.dataset, args.normalize_features)
permute_masks = random_planetoid_splits if args.random_splits else None
run(dataset, Net(dataset), args.runs, args.epochs, args.lr, args.weight_decay,
args.early_stopping, permute_masks)
def create_sgc_model(data_name, lr=0.2, weight_decay=3e-5):
dataset = get_planetoid_dataset(data_name, True)
model = SGC(dataset, 2)
optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
return dataset, model, optimizer
x = self.conv2_ssl(x, added_index) # LAYER 2
return F.log_softmax(x, dim=1), z,r.size(-1)
# ego-Facebook com-Amazon ego-gplus ego-twitter
# name = "ego-Facebook"
# path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', name)
# dataset = SNAPDataset(path, name)
# # pdb.set_trace()
# run(dataset, DoubleNet(dataset, dataset[0].num_feature, int(dataset[0].num_class)), args.runs, args.epochs, args.lr, args.weight_decay,
# args.early_stopping)
motif_size =3
max_value = 1000
gen_interval= n_epochs_gen = 3
dis_interval= n_epochs_dis = 3
batch_size_gen = 64
batch_size_dis = 64
# "CiteSeer", "PubMed"
names = ["CiteSeer"]
for name in names:
dataset = get_planetoid_dataset(name, args.normalize_features)
dataset = dataset.shuffle()
# pdb.set_trace()
run(dataset, DoubleNet(dataset, dataset.num_features, dataset.num_classes), args.runs, args.epochs, args.lr, args.weight_decay,
args.early_stopping)
# layers = [4]
hidden = 16
alphas = [1, 2, 3, 4, 5]
run_num = 100
epoch_num = 300
datasets = ['Cora', 'CiteSeer', 'PubMed']
nets = [
GCNHIGH,
GCNDiff,
GCN,
]
results = []
for dataset_name, Net in product(datasets, nets):
print("{}\n ========".format(dataset_name))
dataset = get_planetoid_dataset(dataset_name, True)
permute_masks = random_planetoid_splits if random_splits else None
if (Net == GCNDiff):
print("GCN Diffusion")
data = dataset[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)
data = gdc(data)
run_(dataset, data, Net(data, dataset, hidden), run_num, epoch_num,
0.01, 0.0005, 10, permute_masks)
elif (Net == GCNHIGH):
print("GCN Highway")
def __init__(self, dataset):
super(Net, self).__init__()
self.crd = CRD(dataset.num_features, args.hidden, args.dropout)
self.cls = CLS(args.hidden, dataset.num_classes)
def reset_parameters(self):
self.crd.reset_parameters()
self.cls.reset_parameters()
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = self.crd(x, edge_index, data.train_mask)
x = self.cls(x, edge_index, data.train_mask)
return x
dataset = get_planetoid_dataset(name=args.dataset, normalize_features=args.normalize_features, split=args.split)
kwargs = {
'dataset': dataset,
'model': Net(dataset),
'str_optimizer': args.optimizer,
'str_preconditioner': args.preconditioner,
'runs': args.runs,
'epochs': args.epochs,
'lr': args.lr,
'weight_decay': args.weight_decay,
'early_stopping': args.early_stopping,
'logger': args.logger,
'momentum': args.momentum,
'eps': args.eps,
'update_freq': args.update_freq,
def create_gfnn_model(data_name, nhid=32, lr=0.2, weight_decay=5e-4):
dataset = get_planetoid_dataset(data_name, True)
model = GFNN(dataset, nhid, 2)
optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
return dataset, model, optimizer
