torch_geometric_signed_directed.nn.signed.SGCNConv

Classes

SGCNConv

The signed graph convolutional operator from the `"Signed Graph

Module Contents

class SGCNConv(in_dim: int, out_dim: int, first_aggr: bool, bias: bool = True, norm_emb: bool = False, **kwargs)

Bases: torch_geometric.nn.conv.MessagePassing

The signed graph convolutional operator from the “Signed Graph Convolutional Network” paper

\[ \begin{align}\begin{aligned}\mathbf{x}_v^{(\textrm{pos})} &= \mathbf{\Theta}^{(\textrm{pos})} \left[ \frac{1}{|\mathcal{N}^{+}(v)|} \sum_{w \in \mathcal{N}^{+}(v)} \mathbf{x}_w , \mathbf{x}_v \right]\\\mathbf{x}_v^{(\textrm{neg})} &= \mathbf{\Theta}^{(\textrm{neg})} \left[ \frac{1}{|\mathcal{N}^{-}(v)|} \sum_{w \in \mathcal{N}^{-}(v)} \mathbf{x}_w , \mathbf{x}_v \right]\end{aligned}\end{align} \]

if first_aggr is set to True, and

\[ \begin{align}\begin{aligned}\mathbf{x}_v^{(\textrm{pos})} &= \mathbf{\Theta}^{(\textrm{pos})} \left[ \frac{1}{|\mathcal{N}^{+}(v)|} \sum_{w \in \mathcal{N}^{+}(v)} \mathbf{x}_w^{(\textrm{pos})}, \frac{1}{|\mathcal{N}^{-}(v)|} \sum_{w \in \mathcal{N}^{-}(v)} \mathbf{x}_w^{(\textrm{neg})}, \mathbf{x}_v^{(\textrm{pos})} \right]\\\mathbf{x}_v^{(\textrm{neg})} &= \mathbf{\Theta}^{(\textrm{pos})} \left[ \frac{1}{|\mathcal{N}^{+}(v)|} \sum_{w \in \mathcal{N}^{+}(v)} \mathbf{x}_w^{(\textrm{neg})}, \frac{1}{|\mathcal{N}^{-}(v)|} \sum_{w \in \mathcal{N}^{-}(v)} \mathbf{x}_w^{(\textrm{pos})}, \mathbf{x}_v^{(\textrm{neg})} \right]\end{aligned}\end{align} \]

otherwise. In case first_aggr is False, the layer expects x to be a tensor where x[:, :in_dim] denotes the positive node features \(\mathbf{X}^{(\textrm{pos})}\) and x[:, in_dim:] denotes the negative node features \(\mathbf{X}^{(\textrm{neg})}\).

Parameters:

  • in_dim (int or tuple) – Size of each input sample, or -1 to derive the size from the first input(s) to the forward method. A tuple corresponds to the sizes of source and target dimensionalities.

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

  • first_aggr (bool) – Denotes which aggregation formula to use.

  • norm_emb (bool, optional) – Whether to normalize embeddings. (default: False)

  • bias (bool, optional) – If set to False, the layer will not learn an additive bias. (default: True)

  • norm_emb (bool) – Denotes embedding is normalized or not. (default: False)

  • **kwargs (optional) – Additional arguments of torch_geometric.nn.conv.MessagePassing.

in_dim
out_dim
first_aggr
norm_emb = False
reset_parameters()
forward(x: torch.Tensor | torch_geometric.typing.PairTensor, pos_edge_index: torch_geometric.typing.Adj, neg_edge_index: torch_geometric.typing.Adj) → torch.Tensor
message(x_j: torch.Tensor) → torch.Tensor
message_and_aggregate(adj_t: torch_geometric.typing.SparseTensor, x: torch_geometric.typing.PairTensor) → torch.Tensor