torch_geometric_signed_directed.nn.signed.SSSNET_link_prediction

Classes

SSSNET_link_prediction

The signed graph link prediction model adapted from the

Module Contents

class SSSNET_link_prediction(nfeat: int, hidden: int, nclass: int, dropout: float, hop: int, fill_value: float, directed: bool = False, bias: bool = True)

Bases: torch.nn.Module

The signed graph link prediction model adapted from the SSSNET: Semi-Supervised Signed Network Clustering paper.

Parameters:

• nfeat (int) – Number of features.

• hidden (int) – Hidden dimensions of the initial MLP.

• nclass (int) – Number of link classes.

• dropout (float) – Dropout probability.

• 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)

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

forward(edge_index_p: torch.LongTensor, edge_weight_p: torch.FloatTensor, edge_index_n: torch.LongTensor, edge_weight_n: torch.FloatTensor, features: torch.FloatTensor, query_edges: torch.LongTensor) → Tuple[torch.FloatTensor, torch.FloatTensor, torch.LongTensor, torch.FloatTensor]

Making a forward pass of the SSSNET.

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.

• features (PyTorch FloatTensor) - Input node features, with shape (num_nodes, num_features).

• query_edges (PyTorch Long Tensor) - Edge indices for querying labels.

Return types:

• log_prob (PyTorch Float Tensor) - Logarithmic class probabilities for all nodes, with shape (num_nodes, num_classes).