gammagl.models.GCNModel

class GCNModel(feature_dim, hidden_dim, num_class, drop_rate=0.2, num_layers=2, norm='both', name=None)

Graph Convolutional Network proposed in “Semi-supervised Classification with Graph Convolutional Networks” paper.

Parameters:

• feature_dim (int) – input feature dimension.

• hidden_dim (int) – hidden dimension.

• num_class (int) – number of classes.

• drop_rate (float, optional) – dropout rate.

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

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

• name (str, optional) – model name.

forward(x, edge_index, edge_weight, num_nodes)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.