Constant Curvature Graph Convolutional Networks

ICLR 2020 Gregor BachmannGary BécigneulOctavian-Eugen Ganea

Interest has been rising lately towards methods representing data in non-Euclidean spaces, e.g. hyperbolic or spherical, that provide specific inductive biases useful for certain real-world data properties, e.g. scale-free, hierarchical or cyclical. However, the popular graph neural networks are currently limited in modeling data only via Euclidean geometry and associated vector space operations... (read more)

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