HyperGraph Neural Diffusion: A Novel Approach to High-Dimensional Data Representation in AI
Abstract
The rapid growth of high-dimensional data demands new representation techniques for efficient processing and analysis. This paper proposes HyperGraph Neural Diffusion (HGND), a novel AI framework that extends traditional graph neural networks using hypergraph diffusion principles. HGND models complex relationships between multiple entities, allowing superior structural learning and feature extraction in domains like bioinformatics, social networks, and financial analytics. Experimental evaluations reveal significant performance improvements over state-of-the-art graph-based AI techniques, making HGND a promising direction for high-dimensional AI applications.
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