  Hypergraphs are a natural and powerful choice for modeling group interactions
in the real world, which are often referred to as higher-order networks. For
example, when modeling collaboration networks, where collaborations can involve
not just two but three or more people, employing 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 learning and mining on hypergraphs, and hypergraph mining,
which seeks to enhance our understanding of underlying systems through
hypergraph modeling, gained increasing attention in research. Researchers have
discovered various structural patterns in real-world hypergraphs, leading to
the development of mining tools. Moreover, they have designed generators with
the aim of reproducing and thereby shedding light on these patterns. In this
survey, we provide a comprehensive overview of the current landscape of
hypergraph mining, covering patterns, tools, and generators. We provide
comprehensive taxonomies for them, and we also provide in-depth discussions to
provide insights into future research on hypergraph mining.
