torch_geometric_signed_directed.nn.directed.DiGCN_Inception_Block

Classes

DiGCN_InceptionBlock

An implementation of the inception block model from the

Module Contents

class DiGCN_InceptionBlock(in_dim: int, out_dim: int)

Bases: torch.nn.Module

An implementation of the inception block model from the Digraph Inception Convolutional Networks paper.

Parameters:

  • in_dim (int) – Dimention of input.

  • out_dim (int) – Dimention of output.

ln
conv1
conv2
reset_parameters()
forward(x: torch.FloatTensor, edge_index: torch.LongTensor, edge_weight: torch.FloatTensor, edge_index2: torch.LongTensor, edge_weight2: torch.FloatTensor) → Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]

Making a forward pass of the DiGCN inception block model.

Arg types:

  • x (PyTorch FloatTensor) - Node features.

  • edge_index, edge_index2 (PyTorch LongTensor) - Edge indices.

  • edge_weight, edge_weight2 (PyTorch FloatTensor) - Edge weights corresponding to edge indices.

Return types:

  • x0, x1, x2 (PyTorch FloatTensor) - Hidden representations.