torch_geometric_signed_directed.nn.directed.DiGCN_node_classification

Classes

DiGCN_node_classification

An implementation of the DiGCN model without inception blocks for node classification from the

Module Contents

class DiGCN_node_classification(num_features: int, hidden: int, label_dim: int, dropout: float = 0.5)

Bases: torch.nn.Module

An implementation of the DiGCN model without inception blocks for node classification from the Digraph Inception Convolutional Networks paper.

Parameters:

  • num_features (int) – Dimension of input features.

  • hidden (int) – Hidden dimension.

  • label_dim (int) – Number of clusters.

  • dropout (float) – Dropout value. (Default: 0.5)

conv1
conv2
dropout = 0.5
reset_parameters()
forward(x: torch.FloatTensor, edge_index: torch.LongTensor, edge_weight: torch.FloatTensor = None) → torch.FloatTensor

Making a forward pass of the DiGCN node classification model without inception blocks.

Arg types:

  • x (PyTorch FloatTensor) - Node features.

  • edge_index (PyTorch LongTensor) - Edge indices.

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

Return types:

  • x (PyTorch FloatTensor) - Logarithmic class probabilities for all nodes, with shape (num_nodes, num_classes).