gammagl.layers.conv.HANConv

class HANConv(in_channels, out_channels, metadata, heads=1, negative_slope=0.2, dropout_rate=0.5)[source]

The Heterogenous Graph Attention Operator from the “Heterogenous Graph Attention Network” paper.

Note

For an example of using HANConv, see examples/han_trainer.py.

Parameters:

  • in_channels (int, dict[str, int]) – Size of each input sample of every node type, or -1 to derive the size from the first input(s) to the forward method.

  • out_channels (int) – Size of each output sample.

  • metadata (tuple[list[str], list[tuple[str, str, str]]]) – The metadata of the heterogeneous graph, i.e. its node and edge types given by a list of strings and a list of string triplets, respectively. See gammagl.data.HeteroGraph.metadata() for more information.

  • heads (int, optional) – Number of multi-head-attentions. (default: 1)

  • negative_slope (float, optional) – LeakyReLU angle of the negative slope. (default: 0.2)

  • dropout (float, optional) – Dropout probability of the normalized attention coefficients which exposes each node to a stochastically sampled neighborhood during training. (default: 0)

  • **kwargs (optional) – Additional arguments of gammagl.layers.conv.MessagePassing.

forward(x_dict, edge_index_dict, num_nodes_dict)[source]