2021
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
ICML 2021spotlight
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 graphs can be effectively represented by an embedding model that combines three components: a pseudo-Riemannian metric…