Hyperbolic geometric spaces are essential for representing hierarchical structures in various graph learning tasks. They address structural anisotropy challenges in latent graph diffusion processes. The HypDiff framework introduces a method for generating hyperbolic Gaussian noise, tackling issues with Gaussian distributions in hyperbolic spaces. This approach applies geometric constraints based on angular similarity to preserve local graph structures.

Implementation of HypDiff in MQL5 began with OpenCL program development. Key components include data projection into hyperbolic space via hyperProjection and HyperProjectionGrad kernels. Further progress involves creating a neuronal class for hyperbolic projection, emphasizing systematic initialization and validation.

The original algorithm demanded centroid determination from training datasets, unsuitab...

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