  This paper introduces the subgraph nomination inference task, in which
example subgraphs of interest are used to query a network for similarly
interesting subgraphs. This type of problem appears time and again in real
world problems connected to, for example, user recommendation systems and
structural retrieval tasks in social and biological/connectomic networks. We
formally define the subgraph nomination framework with an emphasis on the
notion of a user-in-the-loop in the subgraph nomination pipeline. In this
setting, a user can provide additional post-nomination light supervision that
can be incorporated into the retrieval task. After introducing and formalizing
the retrieval task, we examine the nuanced effect that user-supervision can
have on performance, both analytically and across real and simulated data
examples.
