  Research on probabilistic models of networks now spans a wide variety of
fields, including physics, sociology, biology, statistics, and machine
learning. These efforts have produced a diverse ecology of models and methods.
Despite this diversity, many of these models share a common underlying
structure: pairwise interactions (edges) are generated with probability
conditional on latent vertex attributes. Differences between models generally
stem from different philosophical choices about how to learn from data or
different empirically-motivated goals. The highly interdisciplinary nature of
work on these generative models, however, has inhibited the development of a
unified view of their similarities and differences. For instance, novel
theoretical models and optimization techniques developed in machine learning
are largely unknown within the social and biological sciences, which have
instead emphasized model interpretability. Here, we describe a unified view of
generative models for networks that draws together many of these disparate
threads and highlights the fundamental similarities and differences that span
these fields. We then describe a number of opportunities and challenges for
future work that are revealed by this view.
