class pygod.nn.GAEBase(in_dim, hid_dim=64, num_layers=4, dropout=0.0, act=<function relu>, backbone=<class 'torch_geometric.nn.models.basic_gnn.GCN'>, recon_s=False, sigmoid_s=False, **kwargs)[source]#

Bases: Module

Graph Autoencoder

See [KW16] for details.

  • in_dim (int) – Input dimension of model.

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

  • num_layers (int, optional) – Total number of layers in model. Default: 4.

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

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

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

  • recon_s (bool, optional) – Reconstruct the structure instead of node feature . Default: False.

  • sigmoid_s (bool, optional) – Whether to use sigmoid function to scale the reconstructed structure. Default: False.

  • **kwargs (optional) – Other parameters for the backbone.

forward(x, edge_index)[source]#

Forward computation.


x_ – Reconstructed embeddings.

Return type:


static process_graph(data, recon_s=False)[source]#

Obtain the dense adjacency matrix of the graph.