  Real-world complex networks are usually being modeled as graphs. The concept
of graphs assumes that the relations within the network are binary (for
instance, between pairs of nodes); however, this is not always true for many
real-life scenarios, such as peer-to-peer communication schemes, paper
co-authorship, or social network interactions. For such scenarios, it is often
the case that the underlying network is better and more naturally modeled by
hypergraphs. A hypergraph is a generalization of a graph in which a single
(hyper)edge can connect any number of vertices. Hypergraphs allow modelers to
have a complete representation of multi-relational (many-to-many) networks;
hence, they are extremely suitable for analyzing and discovering more subtle
dependencies in such data structures.
  Working with hypergraphs requires new software libraries that make it
possible to perform operations on them, from basic algorithms (such as
searching or traversing the network) to computing significant hypergraph
measures, to including more challenging algorithms (such as community
detection). In this paper, we present a new software library,
SimpleHypergraphs.jl, written in the Julia language and designed for
high-performance computing on hypergraphs and propose two new algorithms for
analyzing their properties: s-betweenness and modified label propagation.
  We also present various approaches for hypergraph visualization integrated
into our tool. In order to demonstrate how to exploit the library in practice,
we discuss two case studies based on the 2019 Yelp Challenge dataset and the
collaboration network built upon the Game of Thrones TV series. The results are
promising and they confirm the ability of hypergraphs to provide more insight
than standard graph-based approaches.
