- class pygod.nn.GAANBase(in_dim, noise_dim, hid_dim=64, num_layers=4, dropout=0.0, act=<function relu>, **kwargs)#
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)#