dgld.models.CONAD.models

class dgld.models.CONAD.models.CONAD(feat_size)[source]

Bases: Module

Contrastive Attributed Network Anomaly Detection with Data Augmentation.[PAKDD 2022] ref:https://github.com/zhiming-xu/conad

Parameters

feat_size (int) – dimension of feature

Examples

>>> from dgld.models.CONAD import CONAD
>>> model = CONAD(feat_size=1433)
>>> model.fit(g, num_epoch=1)
>>> result = model.predict(g)
fit(graph, lr=0.001, weight_decay=0.0, num_epoch=1, margin=0.5, alpha=0.9, eta=0.7, device='cpu', rate=0.2, contrast_type='siamese', batch_size=0)[source]

Fitting model

Parameters

  • graph (dgl.DGLGraph) – graph dataset

  • lr (float, optional) – learning rate, by default 1e-3

  • weight_decay (float, optional) – weight decay (L2 penalty), by default 0.

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

  • margin (float, optional) – parameter of the contrastive loss function, by default 0.5

  • alpha (float, optional) – balance parameter, by default 0.9

  • eta (float, optional) – balance parameter, by default 0.7

  • device (str, optional) – device of computation, by default ‘cpu’

  • rate (float, optional) – the rate of anomalies, by default 0.2

  • contrast_type (str, optional) – categories of contrastive loss functions, by default ‘siamese’

  • batch_size (int, optional) – the size of training batch, by default 0

predict(graph, alpha=0.9, device='cpu', batch_size=0)[source]

predict and return anomaly score of each node

Parameters

  • graph (dgl.DGLGraph) – graph dataset

  • alpha (float, optional) – balance parameter, by default 0.9

  • device (str, optional) – device of computation, by default ‘cpu’

  • batch_size (int, optional) – the size of training batch, by default 0

Returns

anomaly score of each node

Return type

numpy.ndarray

training: bool
class dgld.models.CONAD.models.CONAD_Base(in_feats, hid_feats=128, out_feats=64, num_heads=2, activation=LeakyReLU(negative_slope=0.01))[source]

Bases: Module

This is a basic structure model of CONAD.

Parameters

  • in_feats (int) – the feature dimension of the input data.

  • hid_feats (int, optional) – the dimension of hidden feature, by default 128

  • out_feats (int, optional) – the dimension of output feature, by default 64

  • num_heads (int, optional) – number of heads in Multi-Head Attention.

  • activation (callable activation function/layer or None, optional) – if not None, applies an activation function to the updated node features, by default nn.LeakyReLU()

embed(g, h)[source]

compute embeddings

Parameters

  • g (dgl.DGLGraph or list) – graph dataset

  • h (torch.Tensor) – features of nodes

Returns

embeddings of nodes

Return type

torch.Tensor

embed_batch(blocks, h)[source]

compute embeddings for mini-batch graph training

Parameters

  • g (list) – graph dataset

  • h (torch.Tensor) – features of nodes

Returns

embeddings of nodes

Return type

torch.Tensor

forward(g, h)[source]

Forward Propagation

Parameters

  • g (dgl.DGLGraph) – graph dataset

  • h (torch.Tensor or list) – feature representation of nodes

Returns

  • torch.Tensor – reconstructed adjacency matrix

  • torch.Tensor – reconstructed attribute matrix

forward_batch(blocks, h)[source]

Forward Propagation for mini-batch graph training

Parameters

  • g (dgl.DGLGraph) – graph dataset

  • h (list) – feature representation of nodes

Returns

  • torch.Tensor – reconstructed adjacency matrix

  • torch.Tensor – reconstructed attribute matrix

reconstruct(g, h)[source]

reconstruct attribute matrix and adjacency matrix

Parameters

  • g (dgl.DGLGraph or list) – graph dataset

  • h (torch.Tensor) – feature representation of nodes

Returns

  • torch.Tensor – reconstructed adjacency matrix

  • torch.Tensor – reconstructed attribute matrix

reconstruct_batch(blocks, h)[source]

reconstruct attribute matrix and adjacency matrix for mini-batch graph training

Parameters

  • g (list) – graph dataset

  • h (torch.Tensor) – feature representation of nodes

Returns

  • torch.Tensor – reconstructed adjacency matrix

  • torch.Tensor – reconstructed attribute matrix

training: bool
class dgld.models.CONAD.models.KnowledgeModel(rate, num_added_edge, surround, scale_factor)[source]

Bases: BaseTransform

Knowledge Modeling Module, introduce a certain amount of anomalies belonging to each anomaly type to the input attributed network to form an augmented attributed network

Parameters

  • rate (float) – rate for generating anomalies

  • num_added_edge (int) – parameter for generating high-degree anomalies

  • surround (int) – parameter for generating outlying anomalies

  • scale_factor (float) – parameter for generating disproportionate anomalies

class dgld.models.CONAD.models.SiameseContrastiveLoss(margin)[source]

Bases: Module

siamese contrastive loss function

Parameters

margin (float) – parameter of the contrastive loss function

forward(z, z_hat, l, adj)[source]

Forward Propagation

Parameters

  • z (torch.Tensor) – feature representation of the original graph

  • z_hat (torch.Tensor) – feature representation for data augmentation graphs

  • l (torch.Tensor) – anomaly labels

  • adj (torch.Tensor) – adjacency matrix

Returns

the result of the loss function

Return type

torch.Tensor

training: bool
class dgld.models.CONAD.models.TripletContrastiveLoss(margin)[source]

Bases: Module

triplet contrastive loss function

Parameters

margin (float) – parameter of the contrastive loss function

forward(orig, aug, l, adj)[source]

Forward Propagation

Parameters

  • orig (torch.Tensor) – feature representation of the original graph

  • aug (torch.Tensor) – feature representation for data augmentation graphs

  • l (torch.Tensor) – anomaly labels

  • adj (torch.Tensor) – adjacency matrix

Returns

the result of the loss function

Return type

torch.Tensor

training: bool