models.nn.grand

Module Contents

Classes

MLPLayer
Grand

class models.nn.grand.MLPLayer(in_features, out_features, bias=True)

Bases: torch.nn.Module

reset_parameters(self)

forward(self, x)

__repr__(self)

class models.nn.grand.Grand(nfeat, nhid, nclass, input_droprate, hidden_droprate, use_bn, dropnode_rate, tem, lam, order, sample, alpha)

Bases: models.BaseModel

static add_args(parser)

Add model-specific arguments to the parser.

classmethod build_model_from_args(cls, args)

Build a new model instance.

dropNode(self, x)

normalize_adj(self, edge_index, edge_weight, num_nodes)

rand_prop(self, x, edge_index, edge_weight)

consis_loss(self, logps, train_mask)

normalize_x(self, x)

forward(self, x, edge_index)

adj = torch.sparse_coo_tensor(

edge_index, torch.ones(edge_index.shape[1]).float(), (x.shape[0], x.shape[0]),

).to(x.device)

loss(self, data)

predict(self, data)