BlockDecomposition

class BlockDecomposition(input_dim, num_relations, num_blocks=None, output_dim=None)

Bases: pykeen.nn.message_passing.Decomposition

Represent relation-specific weight matrices via block-diagonal matrices.

Initialize the layer.

Parameters

• input_dim (int) – >0 The input dimension.

• num_relations (int) – >0 The number of relations.

• num_blocks (Optional[int]) – >0 The number of blocks to use. Has to be a divisor of input_dim.

• output_dim (Optional[int]) – >0 The output dimension. If None is given, defaults to input_dim.

Methods Summary

forward(x, node_keep_mask, source, target, …)	Relation-specific message passing from source to target.
reset_parameters()	Reset the parameters of this layer.

Methods Documentation

forward(x, node_keep_mask, source, target, edge_type, edge_weights=None)

Relation-specific message passing from source to target.

Parameters

• x (FloatTensor) – shape: (num_nodes, input_dim) The node representations.

• node_keep_mask (Optional[BoolTensor]) – shape: (num_nodes,) The node-keep mask for self-loop dropout.

• source (LongTensor) – shape: (num_edges,) The source indices.

• target (LongTensor) – shape: (num_edges,) The target indices.

• edge_type (LongTensor) – shape: (num_edges,) The edge types.

• edge_weights (Optional[FloatTensor]) – shape: (num_edges,) Precomputed edge weights.

Return type

FloatTensor

Returns

shape: (num_nodes, output_dim) The enriched node embeddings.

reset_parameters()

Reset the parameters of this layer.