  Graphs are widely used for representing pairwise interactions in complex
systems. Since such real-world graphs are large and often evergrowing, sampling
a small representative subgraph is indispensable for various purposes:
simulation, visualization, stream processing, representation learning,
crawling, to name a few. However, many complex systems consist of group
interactions (e.g., collaborations of researchers and discussions on online Q&A
platforms), and thus they can be represented more naturally and accurately by
hypergraphs (i.e., sets of sets) than by ordinary graphs. Motivated by the
prevalence of large-scale hypergraphs, we study the problem of representative
sampling from real-world hypergraphs, aiming to answer (Q1) what a
representative sub-hypergraph is and (Q2) how we can find a representative one
rapidly without an extensive search. Regarding Q1, we propose to measure the
goodness of a sub-hypergraph by comparing it with the entire hypergraph in
terms of ten graph-level, hyperedge-level, and node-level statistics. Regarding
Q2, we first analyze the characteristics of six intuitive approaches in 11
real-world hypergraphs. Then, based on the analysis, we propose MiDaS, which
draws hyperedges with a bias towards those with high-degree nodes. Through
extensive experiments, we demonstrate that MiDaS is (a) Representative: finding
overall the most representative samples among 13 considered approaches, (b)
Fast: several orders of magnitude faster than the strongest competitors, which
performs an extensive search, and (c) Automatic: rapidly searching a proper
degree of bias.
