A Tensorflow based non-Euclidean deep learning framework
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Why Non-Euclidean Geometry
Considering these simple graph structures shown below. Nodes with same color has 2-hop distance whereas 1-hop distance between nodes with different color. Now how could we embed these structures in Euclidean space while keeping these distance unchanged?
Actually perfect embedding without distortion, appearing naturally in hyperbolic (negative curvature) or spherical (positive curvature) space, is infeasible in Euclidean space [1].
As shown above, due to the high capacity of modeling complex structured data, e.g. scale-free, hierarchical or cyclic, there has been an growing interest in building deep learning models under non-Euclidean geometry, e.g. link prediction [