def builder(self,
hids=[64],
acts=['relu'],
dropout=0.5,
weight_decay=5e-4,
lr=0.01,
kl=5e-4,
gamma=1.,
use_bias=False,
use_tfn=True):
model = get_model("RobustGCN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
kl=kl,
gamma=gamma,
lr=lr,
use_bias=use_bias)
if use_tfn:
model.use_tfn()
return model
def model_step(self,
hids=[16],
acts=['relu'],
dropout=0.,
weight_decay=5e-4,
lr=0.01,
bias=False,
xi=1e-4,
alpha=1.0,
epsilon=5e-2,
num_power_iterations=1):
model = get_model("GraphAT.GCN_VAT", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
xi=xi,
alpha=alpha,
epsilon=epsilon,
num_power_iterations=num_power_iterations,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
return model
def builder(self,
hids=[32],
acts=['relu'],
dropout=0.5,
weight_decay=5e-4,
lr=0.01,
use_bias=True,
output_normalize=False,
aggregator='mean',
num_samples=[15, 5],
use_tfn=True):
model = get_model("GraphSAGE", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
use_bias=use_bias,
aggregator=aggregator,
output_normalize=output_normalize,
num_samples=num_samples)
if use_tfn:
model.use_tfn()
return model
def model_step(self,
hids=[16],
acts=['relu'],
dropout=0.5,
weight_decay=5e-4,
lr=0.01,
bias=False,
xi=1e-6,
p1=1.0,
p2=1.0,
epsilon=3e-2,
sizes=50,
num_power_iterations=1):
model = get_model("BVAT.SBVAT", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
xi=xi,
p1=p1,
p2=p2,
epsilon=epsilon,
num_power_iterations=num_power_iterations,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
return model
def builder(self,
hids=[],
acts=[],
order=2,
norm_mode="PN",
norm_scale=10,
dropout=0.6,
weight_decay=5e-4,
lr=0.005,
use_bias=True):
model = get_model("SGC_PN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
order=order,
norm_mode=norm_mode,
norm_scale=norm_scale,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
use_bias=use_bias)
return model
def model_step(self,
hids=[32],
acts=['relu'],
K=10,
alpha=0.1,
eps_U=0.1,
eps_V=0.1,
lamb_U=0.5,
lamb_V=0.5,
dropout=0.5,
weight_decay=5e-4,
lr=0.01,
bias=False,
name="SAT.SSGC"):
model = get_model(name, self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
K=K,
alpha=alpha,
eps_U=eps_U,
eps_V=eps_V,
lamb_U=lamb_U,
lamb_V=lamb_V,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
return model
def model_step(self,
hids=[32],
acts=['relu'],
dropout=0.5,
weight_decay=5e-4,
lr=0.01,
bias=True,
output_normalize=False,
aggregator='mean',
sizes=[15, 5]):
model = get_model("GraphSAGE", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias,
aggregator=aggregator,
output_normalize=output_normalize,
sizes=sizes)
return model
def model_step(self,
hids=[],
acts=[],
K=2,
norm_mode="PN",
norm_scale=10,
dropout=0.6,
weight_decay=5e-4,
lr=0.005,
bias=True):
model = get_model("SGC_PN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
K=K,
norm_mode=norm_mode,
norm_scale=norm_scale,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
return model
def builder(self,
hids=[64],
acts=['relu'],
alpha=0.1,
K=10,
ppr_dropout=0.,
dropout=0.5,
weight_decay=5e-4,
lr=0.01,
bias=True):
model = get_model("APPNP", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
alpha=alpha,
K=K,
ppr_dropout=ppr_dropout,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias,
approximated=True)
return model
def builder(self,
hids=[32],
num_filters=[8, 8],
acts=[None, None],
dropout=0.8,
weight_decay=5e-4,
lr=0.1,
bias=False,
K=8,
exclude=["num_filters", "acts"],
use_tfn=True):
model = get_model("LGCN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias,
K=K)
if use_tfn:
model.use_tfn()
return model
def model_step(self,
hids=[16],
acts=['relu'],
dropout=0.5,
weight_decay=1e-4,
lr=0.01,
bias=False):
model = get_model("MedianGCN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
return model
def builder(self,
hids=[32],
acts=['relu'],
dropout=0.5,
weight_decay=5e-4,
lr=0.01,
use_bias=False):
model = get_model("FastGCN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
use_bias=use_bias)
return model
def model_step(self,
hids=[],
acts=[],
dropout=0.5,
weight_decay=5e-5,
lr=0.2,
bias=True):
model = get_model("MLP", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
return model
def builder(self,
hids=[],
acts=[],
dropout=0.5,
weight_decay=5e-5,
lr=0.2,
use_bias=True):
model = get_model("SGC", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
use_bias=use_bias)
return model
def builder(self,
hids=[8],
num_heads=[8],
acts=['elu'],
dropout=0.6,
weight_decay=5e-4,
lr=0.01,
include=["num_heads"]):
model = get_model("GAT", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
num_heads=num_heads,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr)
return model
def builder(self,
hids=[64],
acts=['relu'],
dropout=0.5,
weight_decay=5e-5,
lr=0.01,
tperc=0.45,
use_bias=False):
model = get_model("TrimmedGCN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
tperc=tperc,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
use_bias=use_bias)
return model
def model_step(self,
hids=[64],
acts=['relu'],
dropout=0.5,
weight_decay=5e-3,
lr=0.01,
bias=False,
K=10):
model = get_model("DAGNN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias,
K=K)
return model
def builder(self,
hids=[16],
K=3,
acts=['relu'],
dropout=0.5,
weight_decay=5e-4,
lr=0.01,
bias=True):
model = get_model("TAGCN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
K=K,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
return model
def builder(self,
hids=[16],
acts=['relu'],
dropout=0.5,
weight_decay=5e-4,
lr=0.01,
bias=False,
use_tfn=True):
model = get_model("DenseGCN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
if use_tfn:
model.use_tfn()
return model
def model_step(self,
hids=[32],
acts=['relu'],
pdn_hids=32,
dropout=0.5,
weight_decay=5e-5,
lr=0.01,
bias=True):
model = get_model("PDN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
self.graph.num_edge_attrs,
hids=hids,
pdn_hids=pdn_hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
return model
def model_step(self,
hids=[64],
acts=['relu'],
dropout=0.5,
weight_decay=5e-4,
lr=0.01,
kl=5e-4,
gamma=1.,
bias=False):
model = get_model("RobustGCN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
kl=kl,
gamma=gamma,
lr=lr,
bias=bias)
return model
def model_step(self,
hids=[32],
acts=['relu'],
dropout=0.5,
weight_decay=0.,
lr=0.01,
bias=False):
model = get_model("GCN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias,
experimental_run_tf_function=False)
# if LooseVersion(tf.__version__) < LooseVersion("2.2.0") and use_tfn:
# model.use_tfn()
return model
def builder(self,
hids=[],
acts=[],
dropout=0.5,
weight_decay=5e-5,
lr=0.2,
bias=True,
use_tfn=True):
model = get_model("MLP", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
if use_tfn:
model.use_tfn()
return model
def builder(self,
hids=[64],
acts=[None],
dropout=0.5,
lambda_=5.0,
gamma=0.1,
weight_decay=5e-4,
lr=0.01,
use_bias=False):
model = get_model("SimPGCN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
lambda_=lambda_,
gamma=gamma,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
use_bias=use_bias)
return model
def model_step(self,
hids=[256],
acts=['gelu'],
dropout=0.6,
weight_decay=5e-3,
lr=0.001,
bias=True,
alpha=10.0,
tau=2.0):
model = get_model("GMLP", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
tau=tau,
alpha=alpha,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
return model
def model_step(self,
hids=[16],
acts=['relu'],
dropout=0.5,
weight_decay=5e-4,
lr=0.01,
bias=False,
p1=1.4,
p2=0.7):
model = get_model("BVAT.OBVAT", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
self.graph.num_nodes,
p1=p1,
p2=p2,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
return model
def builder(self,
hids=[64],
acts=['relu'],
ppr_dropout=0.,
dropout=0.5,
weight_decay=5e-4,
lr=0.01,
bias=True,
use_tfn=True):
model = get_model("APPNP", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
ppr_dropout=ppr_dropout,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias,
approximated=False)
if use_tfn:
model.use_tfn()
return model
def model_step(self,
hids=[16],
acts=['relu'],
dropout=0.2,
weight_decay=5e-4,
lr=0.01,
bias=False,
gamma=0.01,
eta=0.1):
model = get_model("LATGCN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
self.graph.num_nodes,
gamma=gamma,
eta=eta,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
bias=bias)
return model
def builder(self,
hids=[64],
acts=['relu'],
dropout=0.5,
weight_decay=5e-3,
lr=0.01,
use_bias=False,
K=10,
use_tfn=True):
model = get_model("DAGNN", self.backend)
model = model(self.graph.num_node_attrs,
self.graph.num_node_classes,
hids=hids,
acts=acts,
dropout=dropout,
weight_decay=weight_decay,
lr=lr,
use_bias=use_bias,
K=K)
if use_tfn:
model.use_tfn()
return model
