gammagl.models.CAGCNModel

class CAGCNModel(base_model, feature_dim, num_class, drop_rate, num_layers=2, hidden_dim=64, norm='both', name=None)

calibration GCN proposed in “Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration” paper.

Parameters:

• base_model (nn.Module) – the model to calibrate.

• feature_dim (int) – input feature dimension.

• num_class (int) – number of classes, namely output dimension.

• drop_rate (float) – dropout rate.

• hidden_dim (int) – hidden dimension.

• num_layers (int, optional) – number of layers.

• norm (str, optional) – apply the normalizer.

• name (str, optional) – model name.

forward(cal_edge_index, cal_edge_weight, cal_num_nodes, *args, **kwargs)

The forward function of CAGCN.

Parameters:

• cal_edge_index – cal_model’s edge index

• cal_edge_weight – cal_model’s edge weight, if None set all weight 1.0 default weight setting in gcn_conv

• cal_num_nodes – cal_model’s num nodes

• kwargs (args &&) – all paras required for base_model