CoLABase¶
- class pygod.nn.CoLABase(in_dim, hid_dim=64, num_layers=4, dropout=0.0, act=<function relu>, backbone=<class 'torch_geometric.nn.models.basic_gnn.GCN'>, **kwargs)[source]¶
Bases:
Module
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
CoLA is a contrastive self-supervised learning based method for graph anomaly detection. This implementation is base on random neighbor sampling instead of random walk sampling in the original paper.
See [LLP+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:
4
.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
.**kwargs – Other parameters for the backbone.