Computationally Tractable Riemannian Manifolds for Graph Embeddings

20 Feb 2020Calin CruceruGary BécigneulOctavian-Eugen Ganea

Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum in machine learning due to their desirable geometric inductive biases, e.g., hierarchical structures benefit from hyperbolic geometry. However, going beyond embedding spaces of constant sectional curvature, while potentially more representationally powerful, proves to be challenging as one can easily lose the appeal of computationally tractable tools such as geodesic distances or Riemannian gradients... (read more)

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