pygod.nn.decoder

class pygod.nn.decoder.DotProductDecoder(in_dim, hid_dim=64, num_layers=1, dropout=0.0, act=<function relu>, sigmoid_s=False, backbone=<class 'torch_geometric.nn.models.basic_gnn.GCN'>, **kwargs)

Dot product decoder for the structure reconstruction, which is defined as \(\symbf{A}' = \sigma(\symbf{Z} \symbf{Z}^\intercal)\), where \(\sigma\) is the optional sigmoid function, \(\symbf{Z}\) is the input hidden embedding, and the \(\symbf{A}'\) is the reconstructed adjacency matrix.

Parameters:

• in_dim (int) – Input dimension of node features.

• hid_dim (int, optional) – Hidden dimension of model. Default: 64.

• num_layers (int, optional) – Number of layers in the decoder. Default: 1.

• dropout (float, optional) – Dropout rate. Default: 0..

• act (callable activation function or None, optional) – Activation function if not None. Default: torch.nn.functional.relu.

• sigmoid_s (bool, optional) – Whether to apply sigmoid to the structure reconstruction. Default: False.

• backbone (torch.nn.Module, optional) – The backbone of the deep decoder implemented in PyG. Default: torch_geometric.nn.GCN.

• **kwargs (optional) – Additional arguments for the backbone.

forward(x, edge_index)

Forward computation.

Parameters:

• x (torch.Tensor) – Input node embeddings.

• edge_index (torch.Tensor) – Edge index.

Returns:

s_ – Reconstructed adjacency matrix.

Return type:

torch.Tensor