dgld.models.ALARM.models

A Deep Multi-View Framework for Anomaly Detection on Attributed Networks.

class dgld.models.ALARM.models.ALARM(feat_size, hidden_size, dropout, view_num, agg_type, agg_vec)[source]

Bases: Module

A Deep Multi-View Framework for Anomaly Detection on Attributed Networks.

Parameters

  • feat_size (int) – dimension of feature

  • hidden_size (int) – dimension of hidden embedding, by default: 64

  • dropout (float) – Dropout rate

  • view_num (int) – number of view, by default 3

  • agg_type (int) – aggregator type, by default 0. 0: Concatention | 1: Random aggregation weights | 2: Manual aggregation weights

  • agg_vec (list) – weighted aggregation vector, bydefault: [1,1,1], stands for concatention. This is necessary if agg_type is 2.

fit(graph, lr=0.005, num_epoch=1, alpha=0.8, device='cpu')[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

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

predict(graph, alpha=0.8, 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

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

Returns

anomaly score of each node

Return type

numpy.ndarray

training: bool
class dgld.models.ALARM.models.ALARMModel(feat_size, hidden_size, dropout, view_num, agg_type, agg_vec)[source]

Bases: Module

A Deep Multi-View Framework for Anomaly Detection on Attributed Networks.

Parameters

  • feat_size (int) – dimension of feature

  • hidden_size (int) – dimension of hidden embedding (default: 64)

  • dropout (float) – Dropout rate

  • view_num (int) – number of view, by default 3

  • agg_type (int) – aggregator type, by default 0. 0: Concatention | 1: Random aggregation weights | 2: Manual aggregation weights

  • agg_vec (list) – weighted aggregation vector, bydefault: [1,1,1], stands for concatention. This is necessary if agg_type is 2.

forward(g, h)[source]

Forward Propagation

Parameters

  • g (dgl.DGLGraph) – graph dataset

  • h (torch.Tensor) – features of nodes

Returns

  • struct_reconstructed (torch.Tensor) – Reconstructed adj matrix

  • x_hat (torch.Tensor) – Reconstructed attribute matrix

training: bool
class dgld.models.ALARM.models.AttributeDecoder(nfeat, nhid, dropout, view_num)[source]

Bases: Module

Attribute Decoder of DominantModel

Parameters

  • nfeat (int) – dimension of feature

  • nhid (int) – dimension of hidden embedding

  • dropout (float) – Dropout rate

  • view_num (int) – number of view, by default:3

forward(g, h)[source]

Forward Propagation

Parameters

  • g (dgl.DGLGraph) – graph dataset

  • h (torch.Tensor) – features of nodes

Returns

x – Reconstructed attribute matrix

Return type

torch.Tensor

training: bool
class dgld.models.ALARM.models.Encoder(nfeat, nhid, dropout, view_num, agg_type, agg_vec)[source]

Bases: Module

Encoder of DominantModel

Parameters

  • nfeat (int) – dimension of feature

  • nhid (int) – dimension of hidden embedding

  • dropout (float) – Dropout rate

  • view_num (int) – number of view, by default 3

  • agg_type (int) – aggregator type, by default 0. 0: Concatention | 1: Random aggregation weights | 2: Manual aggregation weights

  • agg_vec (list) – weighted aggregation vector, bydefault: [1,1,1], stands for concatention. This is necessary if agg_type is 2.

forward(g, h)[source]

Forward Propagation

Parameters

  • g (dgl.DGLGraph) – graph dataset

  • h (torch.Tensor) – features of nodes

Returns

x – embedding of nodes

Return type

torch.Tensor

training: bool
class dgld.models.ALARM.models.StructureDecoder(nhid, dropout, view_num)[source]

Bases: Module

Structure Decoder of DominantModel

Parameters

  • nhid (int) – dimension of hidden embedding

  • dropout (float) – Dropout rate

  • view_num (int) – number of view, by default: 3

forward(g, h)[source]

Forward Propagation

Parameters

  • g (dgl.DGLGraph) – graph dataset

  • h (torch.Tensor) – features of nodes

Returns

x – Reconstructed adj matrix

Return type

torch.Tensor

training: bool