Hypergraph backboning
This work provides a principled method for simplifying hypergraph representations, addressing a key challenge for researchers analyzing complex networked systems with higher-order interactions.
The authors propose a non-parametric information-theoretic method to prune redundant structures in hypergraphs, enabling a minimal representation of higher-order interactions. Validation on synthetic and empirical datasets shows substantial sparsification without loss of core structural information.
Hypergraphs provide a natural framework for describing complex networked systems with higher-order, non-dyadic interactions. Due to their high dimensionality and often redundant structure, a key challenge is to develop methods that simplify hypergraph representations while preserving the essential structure of interactions. Here we present a principled, efficient, and non-parametric information-theoretic method for pruning nested and/or redundant structures in hypergraphs, enabling a minimal representation of higher-order interactions in the presence of local heterogeneity. Our approach naturally extends to weighted hypergraphs, where higher-order topology and hyperedge weights combine to identify the system's structural backbone. We validate the method on controlled synthetic hypergraphs and apply it to empirical datasets from diverse domains, demonstrating substantial sparsification without loss of core structural information.