Stochastic network models appear in various applications, including genetics, proteomics, medical imaging, international relationships, brain science, and many more. For example, they can help identify cybersecurity threats and make power grids more robust. However, all these applications rely on mathematical and statistical formulations designed to model underlying processes. In the past two decades, the modeling of networks and subsequent statistical analysis have become more sophisticated. Research has moved beyond studying individual networks to investigating time-varying and multilayer networks, to addressing privacy issues, and to expanding areas of applications. Often, these research threads are pursued separately, but could benefit from consideration collectively. In addition, limitations have become apparent, for example in the study of optimal likelihood-based algorithms that require impractically lengthy and expensive computations.
With this in mind, this workshop will provide a platform for interdisciplinary collaboration, to identify urgent problems facing the field, and to facilitate the exchange of advanced research methodologies for collection and analysis of diverse network data.
By bringing together mathematicians, statisticians, computer scientists, computational biologists, and machine learning researchers, the program aims to foster the development of new interdisciplinary research and education at the intersection of all these fields.
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