  Interactions involving multiple objects simultaneously are ubiquitous across
many domains. The systems these interactions inhabit can be modelled using
hypergraphs, a generalization of traditional graphs in which each edge can
connect any number of nodes. Analyzing the global and static properties of
these hypergraphs has led to a plethora of novel findings regarding how these
modelled systems are structured. However, less is known about the localized
structure of these systems and how they evolve over time. In this paper, we
propose the study of hypergraph ego-networks, a structure that can be used to
model higher-order interactions involving a single node. We also propose the
temporal reconstruction of hypergraph ego-networks as a benchmark problem for
models that aim to predict the local temporal structure of hypergraphs. By
combining a deep learning binary classifier with a hill-climbing algorithm, we
will present a model for reconstructing hypergraph ego-networks by
incorporating structural patterns found across multiple domains.
