A Survey on Hypergraph Mining: Patterns, Tools, and Generators
Geon Lee, Fanchen Bu, Tina Eliassi-Rad, Kijung Shin

TL;DR
This survey comprehensively reviews hypergraph mining, including patterns, tools, and generators, highlighting current methods and future research directions in analyzing complex higher-order networks.
Contribution
It offers a detailed taxonomy and discussion of existing hypergraph mining tools, patterns, and generators, and outlines future research challenges.
Findings
Categorization of hypergraph mining tools into null models, structural elements, and structural quantities.
Overview of hypergraph generators for synthetic data creation.
Identification of open research challenges in hypergraph mining.
Abstract
Hypergraphs, which belong to the family of higher-order networks, are a natural and powerful choice for modeling group interactions in the real world. For example, when modeling collaboration networks, which may involve not just two but three or more people, the use of hypergraphs allows us to explore beyond pairwise (dyadic) patterns and capture groupwise (polyadic) patterns. The mathematical complexity of hypergraphs offers both opportunities and challenges for hypergraph mining. The goal of hypergraph mining is to find structural properties recurring in real-world hypergraphs across different domains, which we call patterns. To find patterns, we need tools. We divide hypergraph mining tools into three categories: (1) null models (which help test the significance of observed patterns), (2) structural elements (i.e., substructures in a hypergraph such as open and closed triangles), and…
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Taxonomy
TopicsComplex Network Analysis Techniques · Data Visualization and Analytics · Data Mining Algorithms and Applications
