What does this research mean for the field?

The fragmented landscape of hypergraph centrality measures can be unified through a taxonomy that classifies them into structural, functional, and contextual categories. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To establish a unifying taxonomy and comparative framework for hypergraph centrality and importance measures across complex systems.

August 24, 2026ACM Computing SurveysOpen Access

A Survey on Centrality and Importance Measures in Hypergraphs: Categorization and Empirical Insights

View Full Paper

Ask AI

Bookmark

Authors

FBFanchen BuNanjing University of Chinese Medicine YKYeongho KimKorea Advanced Institute of Science and Technology AMAtsushi MiyauchiNihon University

Discussion

Member takes

Implication

Systematic review categorizes 39 hypergraph centrality measures into a unified taxonomy, revealing empirical performance trade-offs for analyzing higher-order complex networks.

Key Points

To establish a unifying taxonomy and comparative framework for hypergraph centrality and importance measures across complex systems.
Systematically surveyed 39 distinct hypergraph centrality measures from network science literature.
Classified measures into structural, functional, and contextual categories based on topological properties, dynamical impacts, and external features.
Conducted experimental assessments to evaluate empirical similarity and computational execution time across measures.
Introduced a three-tier taxonomy organizing 39 hypergraph metrics into topology-based structural, dynamics-based functional, and feature-based contextual groups.
Quantified empirical similarity and runtime efficiency across metrics, providing a benchmark roadmap to guide method selection in network applications.

Cite This Study

Bu et al. (2026) studied this question.

synapsesocial.com/papers/6a8c0010bca056c88e6dee60 https://doi.org/10.1145/3843227

View Full Paper

Ask AI

Bookmark

Implication

Systematic review categorizes 39 hypergraph centrality measures into a unified taxonomy, revealing empirical performance trade-offs for analyzing higher-order complex networks.

A Survey on Centrality and Importance Measures in Hypergraphs: Categorization and Empirical Insights

Discussion

Member takes

Implication

Key Points

Cite This Study

A Survey on Centrality and Importance Measures in Hypergraphs: Categorization and Empirical Insights

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider