RGCN

class RGCN(*, triples_factory, embedding_dim=500, num_layers=2, base_entity_initializer=<function xavier_uniform_>, base_entity_initializer_kwargs=None, relation_initializer=<function xavier_uniform_>, relation_initializer_kwargs=None, relation_representations=None, interaction='DistMult', interaction_kwargs=None, 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, regularizer=None, regularizer_kwargs=None, **kwargs)[source]

Bases: pykeen.models.nbase.ERModel[torch.FloatTensor, pykeen.typing.RelationRepresentation, torch.FloatTensor]

An implementation of R-GCN from [schlichtkrull2018].

The Relational Graph Convolutional Network (R-GCN) comprises three parts:

A GCN-based entity encoder that computes enriched representations for entities, cf. pykeen.nn.message_passing.RGCNRepresentations. The representation for entity \(i\) at level \(l \in (1,\dots,L)\) is denoted as \(\textbf{e}_i^l\). The GCN is modified to use different weights depending on the type of the relation.
Relation representations \(\textbf{R}_{r} \in \mathbb{R}^{d \times d}\) is a diagonal matrix that are learned independently from the GCN-based encoder.
An arbitrary interaction model which computes the plausibility of facts given the enriched representations, cf. pykeen.nn.modules.Interaction.

Scores for each triple \((h,r,t) \in \mathcal{K}\) are calculated by using the representations in the final level of the GCN-based encoder \(\textbf{e}_h^L\) and \(\textbf{e}_t^L\) along with relation representation \(\textbf{R}_{r}\). While the original implementation of R-GCN used the DistMult model and we use it as a default, this implementation allows the specification of an arbitrary interaction model.

\[f(h,r,t) = \textbf{e}_h^L \textbf{R}_{r} \textbf{e}_t^L\]