DONEBase

class pygod.nn.DONEBase(x_dim, s_dim, hid_dim=64, num_layers=4, dropout=0.0, act=<function relu>, w1=0.2, w2=0.2, w3=0.2, w4=0.2, w5=0.2, **kwargs)

Bases: Module

Deep Outlier Aware Attributed Network Embedding

DONE consists of an attribute autoencoder and a structure autoencoder. It estimates five losses to optimize the model, including an attribute proximity loss, an attribute homophily loss, a structure proximity loss, a structure homophily loss, and a combination loss. It calculates three outlier scores, and averages them as an overall scores. This model is transductive only.

See [BVM20] for details.

Parameters:

• x_dim (int) – Input dimension of attribute.

• s_dim (int) – Input dimension of structure.

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

• num_layers (int, optional) – Total number of layers in model. A half (floor) of the layers are for the encoder, the other half (ceil) of the layers are for decoders. Default: 4.

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

• weight_decay (float, optional) – Weight decay (L2 penalty). Default: 0..

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

• w1 (float, optional) – Weight of structure proximity loss. Default: 0.2.

• w2 (float, optional) – Weight of structure homophily loss. Default: 0.2.

• w3 (float, optional) – Weight of attribute proximity loss. Default: 0.2.

• w4 (float, optional) – Weight of attribute homophily loss. Default: 0.2.

• w5 (float, optional) – Weight of combination loss. Default: 0.2.

• **kwargs – Other parameters for the backbone.

forward(x, s, edge_index)

Forward computation.

Parameters:

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

• s (torch.Tensor) – Input structure embeddings.

• edge_index (torch.Tensor) – Edge index.

Returns:

• x_ (torch.Tensor) – Reconstructed attribute embeddings.

• s_ (torch.Tensor) – Reconstructed structure embeddings.

• h_a (torch.Tensor) – Attribute hidden embeddings.

• h_s (torch.Tensor) – Structure hidden embeddings.

• dna (torch.Tensor) – Attribute neighbor distance.

• dns (torch.Tensor) – Structure neighbor distance.

loss_func(x, x_, s, s_, h_a, h_s, dna, dns)

Loss function for DONE.

Parameters:

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

• x – Reconstructed attribute embeddings.

• s (torch.Tensor) – Input structure embeddings.

• s – Reconstructed structure embeddings.

• h_a (torch.Tensor) – Attribute hidden embeddings.

• h_s (torch.Tensor) – Structure hidden embeddings.

• dna (torch.Tensor) – Attribute neighbor distance.

• dns (torch.Tensor) – Structure neighbor distance.

Returns:

• loss (torch.Tensor) – Loss value.

• oa (torch.Tensor) – Attribute outlier scores.

• os (torch.Tensor) – Structure outlier scores.

• oc (torch.Tensor) – Combined outlier scores.

static process_graph(data)

Obtain the dense adjacency matrix of the graph.

Parameters:

data (torch_geometric.data.Data) – Input graph.