RGCNRepresentation

class RGCNRepresentation(triples_factory, max_id=None, shape=None, entity_representations=None, entity_representations_kwargs=None, num_layers=2, use_bias=True, activation=None, activation_kwargs=None, edge_dropout=0.4, self_loop_dropout=0.2, edge_weighting=None, decomposition=None, decomposition_kwargs=None, cache=True, **kwargs)[source]

Bases: Representation

Entity representations enriched by R-GCN.

The GCN employed by the entity encoder is adapted to include typed edges. The forward pass of the GCN is defined by:

\[\textbf{e}_{i}^{l+1} = \sigma \left( \sum_{r \in \mathcal{R}}\sum_{j\in \mathcal{N}_{i}^{r}} \frac{1}{c_{i,r}} \textbf{W}_{r}^{l} \textbf{e}_{j}^{l} + \textbf{W}_{0}^{l} \textbf{e}_{i}^{l}\right)\]

where \(\mathcal{N}_{i}^{r}\) is the set of neighbors of node \(i\) that are connected to \(i\) by relation \(r\), \(c_{i,r}\) is a fixed normalization constant (but it can also be introduced as an additional parameter), and \(\textbf{W}_{r}^{l} \in \mathbb{R}^{d^{(l)} \times d^{(l)}}\) and \(\textbf{W}_{0}^{l} \in \mathbb{R}^{d^{(l)} \times d^{(l)}}\) are weight matrices of the l-th layer of the R-GCN.

The encoder aggregates for each node \(e_i\) the latent representations of its neighbors and its own latent representation \(e_{i}^{l}\) into a new latent representation \(e_{i}^{l+1}\). In contrast to standard GCN, R-GCN defines relation specific transformations \(\textbf{W}_{r}^{l}\) which depend on the type and direction of an edge.

Since having one matrix for each relation introduces a large number of additional parameters, the authors instead propose to use a decomposition, cf. pykeen.nn.message_passing.Decomposition.

Instantiate the R-GCN encoder.

Parameters:
  • triples_factory (CoreTriplesFactory) – The triples factory holding the training triples used for message passing.

  • max_id (Optional[int]) – The maximum number of IDs. could either be None (the default), or match the triples factory’s number of entities.

  • shape (Optional[Sequence[int]]) – the shape information. If None, will propagate the shape information of the base entity representations.

  • entity_representations (Union[str, Representation, Type[Representation], None]) – the base entity representations (or a hint for them)

  • entity_representations_kwargs (Optional[Mapping[str, Any]]) – additional keyword-based parameters for the base entity representations

  • num_layers (int) – The number of layers.

  • use_bias (bool) – Whether to use a bias.

  • activation (Union[str, Module, None]) – The activation.

  • activation_kwargs (Optional[Mapping[str, Any]]) – Additional keyword based arguments passed if the activation is not pre-instantiated. Ignored otherwise.

  • edge_dropout (float) – The edge dropout to use. Does not apply to self-loops.

  • self_loop_dropout (float) – The self-loop dropout to use.

  • edge_weighting (Union[str, EdgeWeighting, None]) – The edge weighting mechanism.

  • decomposition (Union[str, Decomposition, None]) – The decomposition, cf. pykeen.nn.message_passing.Decomposition.

  • decomposition_kwargs (Optional[Mapping[str, Any]]) – Additional keyword based arguments passed to the decomposition upon instantiation.

  • kwargs – additional keyword-based parameters passed to Representation.__init__()

  • cache (bool) – whether to cache representations

Raises:

ValueError – If the triples factory creates inverse triples.

Methods Summary

post_parameter_update()

Apply constraints which should not be included in gradients.

reset_parameters()

Reset the module's parameters.

Methods Documentation

post_parameter_update()[source]

Apply constraints which should not be included in gradients.

Return type:

None

reset_parameters()[source]

Reset the module’s parameters.