  What kind of macroscopic structural and dynamical patterns can we observe in
real-world hypergraphs? What can be underlying local dynamics on individuals,
which ultimately lead to the observed patterns, beyond apparently random
evolution?
  Graphs, which provide effective ways to represent pairwise interactions among
entities, fail to represent group interactions (e.g., collaboration of three or
more researchers, etc.). Regarded as a generalization of graphs, hypergraphs
allowing for various sizes of edges prove fruitful in addressing this
limitation. The increased complexity, however, makes it challenging to
understand hypergraphs as thoroughly as graphs.
  In this work, we closely examine seven structural and dynamical properties of
real hypergraphs from six domains. To this end, we define new measures, extend
notions of common graph properties to hypergraphs, and assess the significance
of observed patterns by comparison with a null model and statistical tests.
  We also propose \textsc{HyperFF}, a stochastic model for generating realistic
hypergraphs. Its merits are three-fold: (a) \underline{Realistic:} it
successfully reproduces all seven patterns, in addition to five patterns
established in previous studies, (b) \underline{Self-contained:} unlike
previously proposed models, it does not rely on oracles (i.e., unexplainable
external information) at all, and it is parameterized by just two scalars, and
(c) \underline{Emergent:} it relies on simple and interpretable mechanisms on
individual entities, which do not trivially enforce but surprisingly lead to
macroscopic properties.
