dgld.models.AAGNN.AAGNN_batch

This is a model for large graph training based on the AAGNN model

class dgld.models.AAGNN.AAGNN_batch.AAGNN_batch(feat_size, out_feats=300)[source]

Bases: Module

This is a model for large graph training based on the AAGNN model.

Parameters

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

  • out_feats (int) – The dimension of output feature.

Examples

>>> from DGLD.AAGNN import AAGNN_batch
>>> model = AAGNN_batch(in_feats=in_feats, out_feats=300)
>>> model.fit(graph, num_epoch=30, device='cuda:0', subgraph_size=32)
Graph_batch_sample(graph, node_ids, device, edge_dic)[source]

Here is a function for sampling the large graph data.

Parameters

  • graph (dgl) – The graph data you input.

  • node_ids (numpy) – The id of the target node you want to sample.

  • device (str) – The number of times you want to train the model.

  • edge_dic (dict) – The input node-to-node relationship dictionary.

Returns

  • adj_matrix (numpy.ndarray) – This is an adjacency matrix of sampled subgraphs.

  • eye_matrix (numpy.ndarray) – This is an adjacency matrix of sampled subgraphs.

  • subgraph_feats (numpy.ndarray) – Node feature vector of the subgraph.

  • node_mask (numpy.ndarray) – This is a vector marking which nodes are the target nodes we need.

cal_center(graph, model, device, subgraph_size, edge_dic)[source]

This is a function to calculate the center vector.

Parameters

  • graph (dgl) – The graph data you input.

  • model (tensor) – The model we trained.

  • device (str) – The number of times you want to train the model.

  • subgraph_size (int) – The size of training subgraph.

  • edge_dic (dict) – The input node-to-node relationship dictionary.

Returns

center – The center vector of all samples.

Return type

numpy.ndarray

fit(graph, num_epoch=100, device='cpu', lr=0.0001, subgraph_size=4096)[source]

This is a function used to train the model.

Parameters

  • graph (dgl) – The graph data you input.

  • num_epoch (int) – The number of times you want to train the model.

  • device (str) – The number of times you want to train the model.

  • lr (float) – Learning rate for model training.

subgraph_sizeint

The size of training subgraph.

Return type

None

loss_fun(out, center, model, super_param, device)[source]

This is a function used to calculate the error loss of the model.

Parameters

  • out (tensor) – Output of the model.

  • center (numpy.ndarray) – The center vector of all samples.

  • model (tensor) – The model we trained.

  • super_param (float) – A hyperparameter that takes values in [0, 1].

  • device (str) – The number of times you want to train the model.

Returns

loss – The loss of model output.

Return type

tensor

predict(graph, device='cpu', subgraph_size=4096)[source]

This is a function that loads a trained model for predicting graph data.

Parameters

  • graph (dgl) – The graph data you input.

  • device (str) – The number of times you want to train the model.

  • subgraph_size (int) – The size of training subgraph.

Returns

score – A vector of decimals representing the anomaly score for each node.

Return type

numpy

training: bool
class dgld.models.AAGNN.AAGNN_batch.model_base(in_feats, out_feats)[source]

Bases: Module

This is the basic structure model of AAGNN.

Parameters

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

  • out_feats (int) – The dimension of output feature.

Examples

>>> self.model = model_base(in_feats, out_feats)
anomaly_score(out)[source]

Here is a function that calculates the anomaly score.

Parameters

out (tensor) – Node vector representation output after model training.

Returns

score – Anomaly Score of Nodes.

Return type

numpy.ndarray

forward(adj_matrix, eye_matrix, subgraph_feats, node_mask)[source]

This is a function used to calculate the forward propagation of the model.

Parameters

  • adj_matrix (tensor) – This is an adjacency matrix of sampled subgraphs.

  • eye_matrix (tensor) – This is an adjacency matrix of sampled subgraphs.

  • subgraph_feats (tensor) – Node feature vector of the subgraph.

  • node_mask (tensor) – This is a vector marking which nodes are the target nodes we need.

Returns

h – Results of model forward propagation calculations.

Return type

tensor

get_normal_nodes(node_feats, p, device)[source]

Here is a function that computes normal nodes.

Parameters

  • node_feats (tensor) – All node feature vectors of graph data.

  • p (float) – All node feature vectors of graph data.

  • device (str) – The number of times you want to train the model.

Returns

node_ids – The ID of the positive sample node.

Return type

numpy.ndarray

training: bool