- class pygod.nn.GAANBase(in_dim, noise_dim, hid_dim=64, num_layers=4, dropout=0.0, act=<function relu>, **kwargs)[source]#
Generative Adversarial Attributed Network Anomaly Detection
GAAN is a generative adversarial attribute network anomaly detection framework, including a generator module, an encoder module, a discriminator module, and uses anomaly evaluation measures that consider sample reconstruction error and real sample recognition confidence to make predictions. This model is transductive only.
See [CLW+20] for details.
in_dim (int) – Input dimension of the node features.
noise_dim (int, optional) – Input dimension of the Gaussian random noise. Defaults:
hid_dim (int, optional) – Hidden dimension of model. Default:
num_layers (int, optional) – Total number of layers in model. A half (floor) of the layers are for the generator, the other half (ceil) of the layers are for encoder. Default:
dropout (float, optional) – Dropout rate. Default:
act (callable activation function or None, optional) – Activation function if not None. Default:
**kwargs – Other parameters for the backbone.
- forward(x, noise)[source]#
x (torch.Tensor) – Input attribute embeddings.
noise (torch.Tensor) – Input noise.
x_ (torch.Tensor) – Reconstructed node features.
a (torch.Tensor) – Reconstructed adjacency matrix from real samples.
a_ (torch.Tensor) – Reconstructed adjacency matrix from fake samples.
- static process_graph(data)[source]#
Obtain the dense adjacency matrix of the graph.
data (torch_geometric.data.Data) – Input graph.