Pseudo-Riemannian Embedding Models for Multi-Relational Graph Representations

no code implementations • 2 Dec 2022 • Saee Paliwal, Angus Brayne, Benedek Fabian, Maciej Wiatrak, Aaron Sim

In this paper we generalize single-relation pseudo-Riemannian graph embedding models to multi-relational networks, and show that the typical approach of encoding relations as manifold transformations translates from the Riemannian to the pseudo-Riemannian case.

Graph Embedding Knowledge Graph Completion +1

Directed Graph Embeddings in Pseudo-Riemannian Manifolds

no code implementations • 16 Jun 2021 • Aaron Sim, Maciej Wiatrak, Angus Brayne, Páidí Creed, Saee Paliwal

The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space.

Graph Representation Learning Link Prediction