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arXiv · 2512.00107

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

Jaewan Chun·Fanchen Bu·Yeongho Kim·Atsushi Miyauchi·Francesco Bonchi·Kijung Shin
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Abstract

Identifying central entities and interactions is a fundamental problem in network science. While well-studied for graphs (pairwise relations), many biological and social systems exhibit higher-order interactions best modeled by hypergraphs. This has led to a proliferation of specialized hypergraph centrality measures, but the field remains fragmented and lacks a unifying framework. This paper addresses this gap by providing the first systematic survey of 39 distinct measures. We introduce a novel taxonomy classifying them as: (1) structural (topology-based), (2) functional (impact on system dynamics), or (3) contextual (incorporating external features). We also present an experimental assessment comparing their empirical similarity and computation time. Finally, we discuss applications, establishing a coherent roadmap for future research in this area.

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Jaewan Chun, Fanchen Bu, Yeongho Kim, Atsushi Miyauchi, Francesco Bonchi, Kijung Shin. 2025-11-27. A Survey on Centrality and Importance Measures in Hypergraphs: Categorization and Empirical Insights. https://doi.org/10.1145/3843227

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