[2603.00599] Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger

Computer Science > Artificial Intelligence arXiv:2603.00599 (cs) [Submitted on 28 Feb 2026] Title:Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger Authors:Li Sun, Ming Zhang, Wenxin Jin, Zhongtian Sun, Zhenhao Huang, Hao Peng, Sen Su, Philip Yu View a PDF of the paper titled Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger, by Li Sun and 7 other authors View PDF HTML (experimental) Abstract:Hypergraphs are the natural description of higher-order interactions among objects, widely applied in social network analysis, cross-modal retrieval, etc. Hypergraph Neural Networks (HGNNs) have become the dominant solution for learning on hypergraphs. Traditional HGNNs are extended from message passing graph neural networks, following the homophily assumption, and thus struggle with the prevalent heterophilic hypergraphs that call for long-range dependence modeling. In this paper, we achieve heterophily-agnostic message passing through the lens of Riemannian geometry. The key insight lies in the connection between oversquashing and hypergraph bottleneck within the framework of Riemannian manifold heat flow. Building on this, we propose the novel idea of locally adapting the bottlenecks of different subhypergraphs. The core innovation of the proposed mechanism is the design of an adaptive local (heat) exchanger. Specifically, it captures the rich long-range dependencies via the Robin condition, and preserves the representatio...