Advanced Hypergraph Mining for Web Applications Using Sphere Neural Networks

Sun, Zhongtian; Harit, Anoushka; Yu, Jongmin; Wang, Jingyun; Liò, Pietro

Abstract

Web-based applications often involve analyzing complex multirelational data generated by various domains, including social platforms, bibliographic networks, recommendation systems, and ecommerce 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.

Citation

Sun, Z., Harit, A., Yu, J., Wang, J., & Liò, P. (2025, April). Advanced Hypergraph Mining for Web Applications Using Sphere Neural Networks. Presented at International World Wide Web Conference 2025, Australia

• Presentation Conference Type: Conference Paper (published)
• Conference Name: International World Wide Web Conference 2025
• Start Date: Apr 28, 2025
• Acceptance Date: Jan 20, 2025
• Deposit Date: May 11, 2025
• Peer Reviewed: Peer Reviewed
• Public URL: https://durham-repository.worktribe.com/output/3945079