  Collecting network data directly from network members can be challenging. One
alternative involves inferring a network from observed groups, for example,
inferring a network of scientific collaboration from researchers' observed
paper authorships. In this paper, I explore when an unobserved undirected
network of interest can accurately be inferred from observed groups. The
analysis uses simulations to experimentally manipulate the structure of the
unobserved network to be inferred, the number of groups observed, the extent to
which the observed groups correspond to cliques in the unobserved network, and
the method used to draw inferences. I find that when a small number of groups
are observed, an unobserved network can be accurately inferred using a simple
unweighted two-mode projection, provided that each group's membership closely
corresponds to a clique in the unobserved network. In contrast, when a large
number of groups are observed, an unobserved network can be accurately inferred
using a statistical backbone extraction model, even if the groups' memberships
are mostly random. These findings offer guidance for researchers seeking to
indirectly measure a network of interest using observations of groups.
