  Modeling networks can serve as a means of summarizing high-dimensional
complex systems. Adapting an approach devised for dense, weighted networks, we
propose a new method for generating and estimating unweighted networks. This
approach can describe a broader class of potential networks than existing
models, including those where nodes in different subnetworks connect to one
another via various attachment mechanisms, inducing flexible and varied
community structures. While unweighted edges provide less resolution than
continuous weights, restricting to the binary case permits the use of
likelihood-based estimation techniques, which can improve estimation of nodal
features. The extra flexibility may contribute a different understanding of
network generating structures, particularly for networks with heterogeneous
densities in different regions.
