torch_geometric_signed_directed.nn.signed.SIMPA

Classes

SIMPA

The signed mixed-path aggregation model from the

Module Contents

class SIMPA(hop: int, fill_value: float, directed: bool = False)

Bases: torch.nn.Module

The signed mixed-path aggregation model from the SSSNET: Semi-Supervised Signed Network Clustering paper.

Parameters:

  • hop (int) – Number of hops to consider.

  • fill_value (float) – Value for added self-loops for the positive part of the adjacency matrix.

  • directed (bool, optional) – Whether the input network is directed or not. (default: False)

conv_layer_p
conv_layer_n
forward(edge_index_p: torch.LongTensor, edge_weight_p: torch.FloatTensor, edge_index_n: torch.LongTensor, edge_weight_n: torch.FloatTensor, x_p: torch.FloatTensor, x_n: torch.FloatTensor, x_pt: torch.FloatTensor | None = None, x_nt: torch.FloatTensor | None = None) → Tuple[torch.FloatTensor, torch.FloatTensor, torch.LongTensor, torch.FloatTensor]

Making a forward pass of SIMPA.

Arg types:

  • edge_index_p, edge_index_n (PyTorch FloatTensor) - Edge indices for positive and negative parts.

  • edge_weight_p, edge_weight_n (PyTorch FloatTensor) - Edge weights for positive and nagative parts.

  • x_p (PyTorch FloatTensor) - Souce positive hidden representations.

  • x_n (PyTorch FloatTensor) - Souce negative hidden representations.

  • x_pt (PyTorch FloatTensor, optional) - Target positive hidden representations. Default: None.

  • x_nt (PyTorch FloatTensor, optional) - Target negative hidden representations. Default: None.

Return types:

  • feat (PyTorch FloatTensor) - Embedding matrix, with shape (num_nodes, 2*input_dim) for undirected graphs and (num_nodes, 4*input_dim) for directed graphs.