dgld.models.ComGA.models

ComGA: Community-Aware Attributed Graph Anomaly Detection

class dgld.models.ComGA.models.AttrDecoder(in_feats, n_enc_1, n_enc_2, n_enc_3, dropout)[source]

Bases: Module

AttrDecoder, utilize attribute decoder to take the learned latent representation Z as input to decode them for reconstruction of original nodal attributes.

Parameters

  • in_feats (int) – dimension of feature

  • n_enc_1 (int) – number of encode1 units

  • n_enc_2 (int) – number of encode2 units

  • n_enc_3 (int) – number of encode3 units

  • dropout (float) – Dropout rate

forward(g, z)[source]

Forward Propagation

Parameters

  • g (dgl.DGLGraph) – graph dataset

  • z (torch.Tensor) – the node latent representation

Returns

Reconstructed attribute matrix

Return type

torch.Tensor

training: bool
class dgld.models.ComGA.models.ComGA(num_nodes, num_feats, n_enc_1, n_enc_2, n_enc_3, dropout)[source]

Bases: Module

ComGA: Community-Aware Attributed Graph Anomaly Detection ref:https://github.com/DASE4/ComGA

Parameters

  • num_nodes (int) – number of nodes

  • num_feats (int) – dimension of feature

  • n_enc_1 (int) – number of encode1 units

  • n_enc_2 (int) – number of encode2 units

  • n_enc_3 (int) – number of encode3 units

  • 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.ComGA.models.ComGAModel(num_nodes, num_feats, n_enc_1, n_enc_2, n_enc_3, dropout)[source]

Bases: Module

ComGA is an anomaly detector consisting of a Community Detection Module, a tGCN Module and an Anomaly Detection Module

Parameters

  • num_nodes (int) – number of nodes

  • num_feats (int) – dimension of feature

  • n_enc_1 (int) – number of encode1 units

  • n_enc_2 (int) – number of encode2 units

  • n_enc_3 (int) – number of encode3 units

  • dropout (float) – Dropout rate

forward(g, x, B)[source]

Forward Propagation

Parameters

  • g (dgl.DGLGraph) – graph dataset

  • x (torch.Tensor) – features of nodes

  • B (torch.Tensor) – Modularity Matrix

Returns

  • A_hat (torch.Tensor) – Reconstructed adj matrix

  • X_hat (torch.Tensor) – Reconstructed attribute matrix

  • B_hat (torch.Tensor) – Reconstructed Modularity Matrix

  • z (torch.Tensor) – the node latent representation of tgcnEnc

  • z_a (torch.Tensor) – the node latent representation of CommunityAE

training: bool
class dgld.models.ComGA.models.CommunityAE(num_nodes, n_enc_1, n_enc_2, n_enc_3, dropout)[source]

Bases: Module

Community Detection Module, The modularity matrix B is reconstructed by autoencode to obtain a representation of each node with community information.

Parameters

  • num_nodes (int) – number of nodes

  • n_enc_1 (int) – number of encode1 units

  • n_enc_2 (int) – number of encode2 units

  • n_enc_3 (int) – number of encode3 units

  • dropout (float) – Dropout rate

forward(B)[source]

Forward Propagation

Parameters

B (torch.Tensor) – Modularity Matrix

Returns

  • hidden1 (torch.Tensor) – the node latent representation of encoder1

  • hidden2 (torch.Tensor) – the node latent representation of encoder2

  • z_a (torch.Tensor) – the node latent representation of encoder3

  • community_reconstructions (torch.Tensor) – Reconstructed Modularity Matrix

training: bool
class dgld.models.ComGA.models.StruDecoder(dropout)[source]

Bases: Module

StruDecoder, utilize structure decoder to take the learned latent representation Z as input to decode them for reconstruction of original graph structure.

Parameters

dropout (float) – Dropout rate

forward(z)[source]

Forward Propagation

Parameters

z (torch.Tensor) – the node latent representation

Returns

Reconstructed adj matrix

Return type

torch.Tensor

training: bool
dgld.models.ComGA.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)
class dgld.models.ComGA.models.tGCNEncoder(in_feats, n_enc_1, n_enc_2, n_enc_3, dropout)[source]

Bases: Module

tGCNEncoder, To effectively fuse community structure information to GCN model for structure anomaly, and learn more distinguishable anomalous node representations for local, global, and structure anomalies.

Parameters

  • in_feats (int) – dimension of feature

  • n_enc_1 (int) – number of encode1 units

  • n_enc_2 (int) – number of encode2 units

  • n_enc_3 (int) – number of encode3 units

  • dropout (float) – Dropout rate

forward(g, x, B_enc1, B_enc2, B_enc3)[source]

Forward Propagation

Parameters

  • g (dgl.DGLGraph) – graph dataset

  • x (torch.Tensor) – features of nodes

  • B_enc1 (torch.Tensor) – the node latent representation of CommunityAE encoder1

  • B_enc2 (torch.Tensor) – the node latent representation of CommunityAE encoder2

  • B_enc3 (torch.Tensor) – the node latent representation of CommunityAE encoder3

Returns

the node latent representation of tGCNEncoder

Return type

torch.Tensor

training: bool