Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum machine learning due to their desirable geometric inductive biases (e.g., hierarchical structures benefit from hyperbolic geometry). However, going beyond embedding spaces constant sectional curvature, while potentially more representationally powerful, proves be challenging one ca...