Hetero Graph-Convolutional Layers

HeteroGraphConv(itr; aggr = +)
HeteroGraphConv(pairs...; aggr = +)

A convolutional layer for heterogeneous graphs.

The itr argument is an iterator of pairs of the form edge_t => layer, where edge_t is a 3-tuple of the form (src_node_type, edge_type, dst_node_type), and layer is a convolutional layers for homogeneous graphs.

Each convolution is applied to the corresponding relation. Since a node type can be involved in multiple relations, the single convolution outputs have to be aggregated using the aggr function. The default is to sum the outputs.

Forward Arguments

• g::GNNHeteroGraph: The input graph.
• x::Union{NamedTuple,Dict}: The input node features. The keys are node types and the values are node feature tensors.

Examples

julia> g = rand_bipartite_heterograph((10, 15), 20)
GNNHeteroGraph:
  num_nodes: Dict(:A => 10, :B => 15)
  num_edges: Dict((:A, :to, :B) => 20, (:B, :to, :A) => 20)

julia> x = (A = rand(Float32, 64, 10), B = rand(Float32, 64, 15));

julia> layer = HeteroGraphConv((:A, :to, :B) => GraphConv(64 => 32, relu),
                               (:B, :to, :A) => GraphConv(64 => 32, relu));

julia> y = layer(g, x); # output is a named tuple

julia> size(y.A) == (32, 10) && size(y.B) == (32, 15)
true