A unified view of generative models for networks: models, methods, opportunities, and challenges
This work provides a cross-disciplinary synthesis for researchers in physics, sociology, biology, statistics, and machine learning, though it is incremental as it organizes existing knowledge rather than introducing new methods.
The paper addresses the lack of a unified view across diverse fields on generative models for networks, which share a common structure of edges conditional on latent vertex attributes, by proposing a framework to highlight similarities and differences and outlining future opportunities and challenges.
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.