  Communities are a common and widely studied structure in networks, typically
under the assumption that the network is fully and correctly observed. In
practice, network data are often collected by querying nodes about their
connections. In some settings, all edges of a sampled node will be recorded,
and in others, a node may be asked to name its connections. These sampling
mechanisms introduce noise and bias which can obscure the community structure
and invalidate assumptions underlying standard community detection methods. We
propose a general model for a class of network sampling mechanisms based on
recording edges via querying nodes, designed to improve community detection for
network data collected in this fashion. We model edge sampling probabilities as
a function of both individual preferences and community parameters, and show
community detection can be performed by spectral clustering under this general
class of models. We also propose, as a special case of the general framework, a
parametric model for directed networks we call the nomination stochastic block
model, which allows for meaningful parameter interpretations and can be fitted
by the method of moments. Both spectral clustering and the method of moments in
this case are computationally efficient and come with theoretical guarantees of
consistency. We evaluate the proposed model in simulation studies on both
unweighted and weighted networks and apply it to a faculty hiring dataset,
discovering a meaningful hierarchy of communities among US business schools.
