Advanced Hypergraph Mining for Web Applications Using Sphere Neural Networks

Web-based applications often involve analyzing complex multi-relational data generated by various domains, including social platforms, bibliographic networks, recommendation systems, and e-commerce platforms. Traditional graph-based methods struggle to model interactions beyond simple pairwise relationships, such as higher-order dependencies and the underlying geometric and structural properties of the data. This paper presents a novel application of hyperspherical deep learning to hypergraphs, integrating geometric hypergraph mining with a Sphere Neural Network (SNN) to model and analyze these intricate relationships effectively. Using real-world datasets, including Reddit, DBLP, MovieLens, and Amazon Co-purchase, our framework embeds hypergraphs into hyperspherical spaces, preserving both relational and geometric properties. Experimental results demonstrate that our method significantly improves performance on tasks such as recommendation, co-purchase prediction, and user behavior analysis, outperforming state-of-the-art techniques. This work highlights the potential of integrating geometric hypergraphs and hyperspherical deep learning to advance the analysis of web-based data.