GADNR#

class pygod.detector.GADNR(hid_dim=64, num_layers=1, deg_dec_layers=4, fea_dec_layers=3, backbone=<class 'torch_geometric.nn.models.basic_gnn.GCN'>, sample_size=2, sample_time=3, neigh_loss='KL', lambda_loss1=0.01, lambda_loss2=0.1, lambda_loss3=0.8, real_loss=True, lr=0.01, epoch=100, dropout=0.0, weight_decay=0.0003, act=<function relu>, gpu=-1, batch_size=0, num_neigh=-1, contamination=0.1, verbose=0, save_emb=False, compile_model=False, **kwargs)[source]#

Bases: DeepDetector

Graph Anomaly Detection via Neighborhood Reconstruction

GAD-NR is a new type of GAE based on neighborhood reconstruction for graph anomaly detection. GAD-NR aims to reconstruct the entire neighborhood (including local structure, self attributes, and neighbors attributes) around a node based on the corresponding node representation.

See [RSL+24] for details.

Parameters:

  • hid_dim (int, optional) – Hidden dimension of model. Default: 64.

  • num_layers (int, optional) – Total number of layers in the backbone encoder model. Default: 1.

  • deg_dec_layers (int, optional) – The number of layers for the node degree decoder. Default: 4.

  • fea_dec_layers (int, optional) – The number of layers for the node feature decoder. Default: 3.

  • backbone (torch.nn.Module, optional) – The backbone of the deep detector implemented in PyG. Default: torch_geometric.nn.GCN.

  • sample_size (int, optional) – The number of samples for the neighborhood distribution. Default: 2.

  • sample_time (int, optional) – The number sample times to remove the noise during node feature and neighborhood distribution reconstruction. Default: 3.

  • neigh_loss (str, optional) – The neighbor reconstruction loss. KL represents the KL divergence loss, W2 represents the W2 loss. Default: KL.

  • lambda_loss1 (float, optional) – The weight of the neighborhood reconstruction loss term. Default: 1e-2.

  • lambda_loss2 (float, optional) – The weight of the node feature reconstruction loss term. Default: 1e-3.

  • lambda_loss3 (float, optional) – The weight of the node degree reconstruction loss term. Default: 1e-4.

  • real_loss (bool, optional) – Whether using the original loss proposed in the paper as the decision score, if not, using the proposed weighted decision score. Default: True.

  • lr (float, optional) – Learning rate. Default: 0.01.

  • epoch (int, optional) – Maximum number of training epoch. Default: 100.

  • dropout (float, optional) – Dropout rate. Default: 0..

  • weight_decay (float, optional) – Weight decay (L2 penalty). Default: 0.0003.

  • act (callable activation function or None, optional) – Activation function if not None. Default: torch.nn.functional.relu.

  • gpu (int) – GPU Index, -1 for using CPU. Default: -1.

  • batch_size (int, optional) – Minibatch size, 0 for full batch training. Default: 0.

  • num_neigh (int, optional) – Number of neighbors in sampling, -1 for all neighbors. Default: -1.

  • contamination (float, optional) – The amount of contamination of the dataset in (0., 0.5], i.e., the proportion of outliers in the dataset. Used when fitting to define the threshold on the decision function. Default: 0.1.

  • verbose (int, optional) – Verbosity mode. Range in [0, 3]. Larger value for printing out more log information. Default: 0.

  • save_emb (bool, optional) – Whether to save the embedding. Default: False.

  • compile_model (bool, optional) – Whether to compile the model with torch_geometric.compile. Default: False.

  • **kwargs (optional) – Other parameters for the backbone.

decision_score_#

The outlier scores of the training data. Outliers tend to have higher scores. This value is available once the detector is fitted.

Type:

torch.Tensor

threshold_#

The threshold is based on contamination. It is the \(N \times\) contamination most abnormal samples in decision_score_. The threshold is calculated for generating binary outlier labels.

Type:

float

label_#

The binary labels of the training data. 0 stands for inliers and 1 for outliers. It is generated by applying threshold_ on decision_score_.

Type:

torch.Tensor

emb#

The learned node hidden embeddings of shape \(N \times\) hid_dim. Only available when save_emb is True. When the detector has not been fitted, emb is None. When the detector has multiple embeddings, emb is a tuple of torch.Tensor.

Type:

torch.Tensor or tuple of torch.Tensor or None

fit(data, label=None, h_loss_weight=1.0, degree_loss_weight=0.0, feature_loss_weight=2.5, loss_step=20)[source]#

Overwrite the base model fit function since GAD-NR uses multiple personalized loss functions.

Parameters:

  • data (torch_geometric.data.Data) – Input graph.

  • label (torch.Tensor, optional) – The optional outlier ground truth labels used for testing. Default: None.

  • h_loss_weight (float, optional) – The weight of the neighborhood reconstruction loss term used in the weighted decision score. Default: 1.0.

  • degree_loss_weight (float, optional) – The weight of the node degree reconstruction loss term used in the weighted decision score. Default: 0..

  • feature_loss_weight (float, optional) – The weight of the node feature reconstruction loss term used in the weighted decision score. Default: 2.5.

  • loss_step (int, optional) – The epoch interval to update the loss terms. Default: 20.

predict(data=None, label=None, return_pred=True, return_score=False, return_prob=False, prob_method='linear', return_conf=False, return_emb=False)#

Prediction for testing data using the fitted detector. Return predicted labels by default.

Parameters:

  • data (torch_geometric.data.Data, optional) – The testing graph. If None, the training data is used. Default: None.

  • label (torch.Tensor, optional) – The optional outlier ground truth labels used for testing. Default: None.

  • return_pred (bool, optional) – Whether to return the predicted binary labels. The labels are determined by the outlier contamination on the raw outlier scores. Default: True.

  • return_score (bool, optional) – Whether to return the raw outlier scores. Default: False.

  • return_prob (bool, optional) – Whether to return the outlier probabilities. Default: False.

  • prob_method (str, optional) –

    The method to convert the outlier scores to probabilities. Two approaches are possible:

    1. 'linear': simply use min-max conversion to linearly transform the outlier scores into the range of [0,1]. The model must be fitted first.

    2. 'unify': use unifying scores, see [KKSZ11].

    Default: 'linear'.

  • return_conf (boolean, optional) – Whether to return the model’s confidence in making the same prediction under slightly different training sets. See [PVD20]. Default: False.

  • return_emb (bool, optional) – Whether to return the learned node representations. Default: False.

Returns:

  • pred (torch.Tensor) – The predicted binary outlier labels of shape \(N\). 0 stands for inliers and 1 for outliers. Only available when return_label=True.

  • score (torch.Tensor) – The raw outlier scores of shape \(N\). Only available when return_score=True.

  • prob (torch.Tensor) – The outlier probabilities of shape \(N\). Only available when return_prob=True.

  • conf (torch.Tensor) – The prediction confidence of shape \(N\). Only available when return_conf=True.