# Hypergraph Representation Learning

The work introduces BHyGNN+, a self-supervised framework for heterophilic hypergraph representation learning that contrasts augmented views of a hypergraph against its dual (with node and hyperedge roles interchanged) using cosine similarity, learning representations without ground-truth labels and without negative samples, and achieving node-classification accuracy above supervised and self-supervised baselines on eleven benchmark datasets covering heterophilic and homophilic hypergraphs plus a synthetic heterophilic dataset.

The work introduces BHyGNN+, a self-supervised framework for heterophilic hypergraph representation learning that contrasts augmented views of a hypergraph against its dual (with node and hyperedge roles interchanged) using cosine similarity, learning representations without ground-truth labels and without negative samples, and achieving node-classification accuracy above supervised and self-supervised baselines on eleven benchmark datasets covering heterophilic and homophilic hypergraphs plus a synthetic heterophilic dataset.

The work presents VyPER, a geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology, combining supervised classification of hyperedges for assigning measured jets and charged leptons to parent particles with a diffusion model for predicting unmeasured neutrino kinematics, optimized jointly through a joint loss function within a unified framework, and compares its performance to existing analytical and machine-learning-based reconstruction techniques across several proton-proton collision processes, indicating that accurate event reconstruction is achievable across a diverse range of Standard Model physics processes.

The work presents VyPER, a geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology, combining supervised classification of hyperedges for assigning measured jets and charged leptons to parent particles with a diffusion model for predicting unmeasured neutrino kinematics, optimized jointly through a joint loss function within a unified framework, and compares its performance to existing analytical and machine-learning-based reconstruction techniques across several proton-proton collision processes, indicating that accurate event reconstruction is achievable across a diverse range of Standard Model physics processes.