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