OCGNNBase¶
- class pygod.nn.OCGNNBase(in_dim, hid_dim, num_layers=2, dropout=0.0, act=<function relu>, backbone=<class 'torch_geometric.nn.models.basic_gnn.GCN'>, beta=0.5, warmup=2, eps=0.001, **kwargs)[source]¶
Bases:
ModuleOne-Class Graph Neural Networks for Anomaly Detection in Attributed Networks
OCGNN is an anomaly detector that measures the distance of anomaly to the centroid, in a similar fashion to the support vector machine, but in the embedding space after feeding towards several layers of GCN.
See [WJD+21] for details.
- Parameters:
in_dim (int) – Input dimension of model.
hid_dim (int, optional) – Hidden dimension of model. Default:
64.num_layers (int, optional) – Total number of layers in model. Default:
2.dropout (float, optional) – Dropout rate. Default:
0..act (callable activation function or None, optional) – Activation function if not None. Default:
torch.nn.functional.relu.backbone (torch.nn.Module) – The backbone of the deep detector implemented in PyG. Default:
torch_geometric.nn.GCN.beta (float, optional) – The weight between the reconstruction loss and radius. Default:
0.5.warmup (int, optional) – The number of epochs for warm-up training. Default:
2.eps (float, optional) – The slack variable. Default:
0.001.**kwargs – Other parameters for the backbone model.