  This paper introduces gradient, adjoint, and $p$-Laplacian definitions for
oriented hypergraphs as well as differential and averaging operators for
unoriented hypergraphs. These definitions are used to define gradient flows in
the form of diffusion equations with applications in modelling group dynamics
and information flow in social networks as well as performing local and
non-local image processing.
