3 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.SI2022
Pseudo-Riemannian Embedding Models for Multi-Relational Graph Representations
Saee Paliwal, Angus Brayne, Benedek Fabian +2
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 manif…
stat.ML2021★ 3 cited
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
Aaron Sim, Maciej Wiatrak, Angus Brayne +2
The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space. In this paper, we show that general directed…
cs.LG2018
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
Daniel Neil, Joss Briody, Alix Lacoste +3
In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowled…