SuperHyperGraph Attention Networks

Resumen

Graph Attention Networks (GAT) employ self-attention to aggregate neighboring node features in
graphs, effectively capturing structural dependencies. HyperGraph Attention Networks (HGAT) extend this
mechanism to hypergraphs by alternating attention-based vertex-to-hyperedge and hyperedge-to-vertex up-
dates, modeling higher-order relationships. In this work, we introduce the n-SuperHyperGraph Attention Net-
work, which leverages SuperHyperGraphs—a hierarchical generalization of hypergraphs—to perform multi-tier
attention among supervertices and superedges. Our investigation is purely theoretical; empirical validation via
computational experiments is left for future study.