rllm.nn.conv.graph_conv.GCNConv

class rllm.nn.conv.graph_conv.GCNConv(in_dim: int, out_dim: int, bias: bool = True, normalize: bool = False)[source]

Bases: MessagePassing

The GCN (Graph Convolutional Network) model implementation with message passing, based on the “Semi-supervised Classification with Graph Convolutional Networks” paper.

This model applies convolution operations to graph-structured data, allowing for the aggregation of feature information from neighboring nodes.

\[\mathbf{X}^{\prime} = \mathbf{\tilde{A}} \mathbf{X} \mathbf{W}\]
Parameters:

  • in_dim (int) – Size of each input sample.

  • out_dim (int) – Size of each output sample.

  • bias (bool) – If set to False, no bias terms are added into the final output.

  • normalize (bool) – If set to True, the adjacency matrix is normalized using the symmetric normalization method. The normalization is performed as follows: \(\mathbf{\tilde{A}} = \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2}\). where \(\mathbf{D}\) is the degree matrix of the graph.

Shapes:

  • input:

    node features \((|\mathcal{V}|, F_{in})\)

    edge_index is sparse adjacency matrix \((|\mathcal{V}|, |\mathcal{V}|)\) or edge list \((2, |\mathcal{E}|)\)

  • output:

    node features \((|\mathcal{V}|, F_{out})\)

forward(x: Tensor, edge_index: Tensor, edge_weight: Tensor | None = None, dim_size: int | None = None) → Tensor[source]

Apply a single GCN message-passing step.

Parameters:

  • x (Tensor) – Input node features with shape [num_nodes, in_dim].

  • edge_index (Union[Tensor, SparseTensor]) – Graph connectivity in edge-list or sparse adjacency format.

  • edge_weight (Optional[Tensor]) – Optional edge weights used during neighborhood aggregation.

  • dim_size (Optional[int]) – Optional number of destination nodes for aggregation output shape inference.

Returns:

Output node features with shape [num_nodes, out_dim].

Return type:

Tensor

Example

>>> import torch
>>> from rllm.nn.conv.graph_conv import GCNConv
>>> conv = GCNConv(16, 8)
>>> x = torch.randn(5, 16)
>>> edge_index = torch.tensor([[0, 1, 2], [1, 2, 3]])
>>> out = conv(x, edge_index)
>>> out.shape
torch.Size([5, 8])