gammagl.layers.conv.HGTConv

class HGTConv(in_channels, out_channels, metadata, heads: int = 1, group: str = 'sum', dropout_rate=0.0)[source]

The Heterogeneous Graph Transformer (HGT) operator from the “Heterogeneous Graph Transformer” paper.

Parameters:

  • in_channels (int, dsict[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)

  • group (str, optional) – The aggregation scheme to use for grouping node embeddings generated by different relations. ("sum", "mean", "min", "max"). (default: "sum")

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

forward(x_dict, edge_index_dict)[source]
propagate(edge_index, aggr='sum', **kwargs)[source]

Function that perform message passing.

Parameters:

  • x (tensor) – input node feature.

  • edge_index (tensor) – edges from src to dst.

  • aggr (str, optional) – aggregation type, default=’sum’, optional=[‘sum’, ‘mean’, ‘max’].

  • fuse_kernel (bool, optional) – use fused kernel function to speed up, default = False.

  • kwargs (optional) – other parameters dict.

message(k_j, q_i, v_j, rel, target_index, num_nodes)[source]

Function that construct message from source nodes to destination nodes.

Parameters:

  • x (tensor) – input node feature.

  • edge_index (tensor) – edges from src to dst.

  • edge_weight (tensor, optional) – weight of each edge.

Returns:

  • tensor – output message

  • Returns – the message matrix, and the shape is [num_edges, message_dim]