dgld.models.AnomalyDAE.models

AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks

class dgld.models.AnomalyDAE.models.AnomalyDAE(feat_size, num_nodes, embed_dim, out_dim, dropout)[source]

Bases: Module

AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks ref:https://github.com/haoyfan/AnomalyDAE

Parameters

  • feat_size (int) – dimension of feature

  • num_nodes (int) – number of nodes

  • embed_dim (int) – dimension of hidden embedding (default: 256)

  • out_dim (int) – dimension of output embedding (default: 128)

  • dropout (float) – Dropout rate

fit(graph, lr=0.005, num_epoch=1, alpha=0.7, eta=5.0, theta=40.0, device='cpu', patience=10)[source]

Fitting model

Parameters

  • graph (dgl.DGLGraph) – graph dataset

  • lr (float, optional) – learning rate, by default 5e-3

  • num_epoch (int, optional) – number of training epochs , by default 1

  • alpha (float, optional) – balance parameter, by default 0.8

  • eta (float, optional) – Attribute penalty balance parameter, by default 5.0

  • theta (float, optional) – structure penalty balance parameter, by default 40.0

  • device (str, optional) – cuda id, by default ‘cpu’

  • patience (int, optional) – early stop patience , by default 10

predict(graph, alpha=0.7, eta=5.0, theta=40.0, device='cpu')[source]

predict and return anomaly score of each node

Parameters

  • graph (dgl.DGLGraph) – graph dataset

  • alpha (float, optional) – balance parameter, by default 0.8

  • eta (float, optional) – Attribute penalty balance parameter, by default 5.0

  • theta (float, optional) – structure penalty balance parameter, by default 40.0

  • device (str, optional) – cuda id, by default ‘cpu’

Returns

anomaly score of each node

Return type

numpy.ndarray

training: bool
class dgld.models.AnomalyDAE.models.AnomalyDAEModel(in_feat_dim, in_num_dim, embed_dim, out_dim, dropout)[source]

Bases: Module

AdnomalyDAE is an anomaly detector consisting of a structure autoencoder, and an attribute reconstruction autoencoder.

Parameters

  • in_feat_dim (int) – Dimension of input feature

  • in_num_dim (int) – Dimension of the input number of nodes

  • embed_dim (int) – Dimension of the embedding after the first reduced linear layer (D1)

  • out_dim (int) – Dimension of final representation

  • dropout (float, optional) – Dropout rate of the model Default: 0

forward(g, x)[source]

Forward Propagation

Parameters

  • g (dgl.DGLGraph) – graph dataset

  • x (torch.Tensor) – features of nodes

Returns

  • A_hat (torch.Tensor) – Reconstructed adj matrix

  • X_hat (torch.Tensor) – Reconstructed attribute matrix

training: bool
class dgld.models.AnomalyDAE.models.AttributeAE(in_dim, embed_dim, out_dim, dropout)[source]

Bases: Module

Attribute Autoencoder in AnomalyDAE model: the encoder employs two non-linear feature transform to the node attribute x. The decoder takes both the node embeddings from the structure autoencoder and the reduced attribute representation to reconstruct the original node attribute.

Parameters

  • in_dim (int) – dimension of the input number of nodes

  • embed_dim (int) – the latent representation dimension of node (after the first linear layer)

  • out_dim (int) – the output dim after two linear layers

  • dropout (float) – dropout probability for the linear layer

forward(x, struct_embed)[source]

Forward Propagation

Parameters

  • x (torch.Tensor) – features of nodes

  • struct_embed (torch.Tensor) – Embedd nodes after the attention layer

Returns

x – Reconstructed attribute (feature) of nodes.

Return type

torch.Tensor

training: bool
class dgld.models.AnomalyDAE.models.NodeAttention(embed_dim, out_sz, nb_nodes, dropout=0.0, act=<function elu>, **kwargs)[source]

Bases: Module

node attention layer

Parameters

  • embed_dim (int) – the latent representation dimension of node

  • out_sz (int) – the output dim after the graph attention layer

  • nb_nodes (int) – number of nodes

  • dropout (float, optional) – dropout probability for the linear layer, by default 0.

  • act (F, optional) – Choice of activation function , by default F.elu

forward(inputs, adj)[source]

Forward Propagation

Parameters

  • inputs (torch.Tensor) – features of nodes

  • adj (numpy.matrix) – adj matrix

Returns

Embedd nodes after the attention layer

Return type

torch.Tensor

training: bool
class dgld.models.AnomalyDAE.models.StructureAE(in_dim, in_num_dim, embed_dim, out_dim, dropout)[source]

Bases: Module

Structure Autoencoder in AnomalyDAE model: the encoder transforms the node attribute X into the latent representation with the linear layer, and a graph attention layer produces an embedding with weight importance of node neighbors. Finally, the decoder reconstructs the final embedding to the original. See :cite:`fan2020anomalydae` for details.

Parameters

  • in_dim (int) – input dimension of node data

  • in_num_dim (int) – number of nodes

  • embed_dim (int) – the latent representation dimension of node (after the first linear layer)

  • out_dim (int) – the output dim after the graph attention layer

  • dropout (float) – dropout probability for the linear layer

forward(g, x)[source]

Forward Propagation

Parameters

  • g (dgl.DGLGraph) – graph dataset

  • x (torch.Tensor) – features of nodes

Returns

  • x (torch.Tensor) – Reconstructed attribute (feature) of nodes.

  • embed_x (torch.Tensor) – Embedd nodes after the attention layer

training: bool
dgld.models.AnomalyDAE.models.init_weights(module: Module) → None[source]

Init Module Weights

Parameters

module (nn.Module) – models to initialize linear weight

Examples

>>> for module in self.modules():
>>>    init_weights(module)