Reception to follow.
It has been nearly 20 years now since what might be described as a revolution in complex networks arguably began. With dozens of disciplines engaged in doing `network science', the impact of this revolution on science in general has been profound. Of course, with most of this work being data-centric, statistical methods and modeling have been central to these efforts. But how much do we truly understand the fundamental implications of having network-structured data on statistical principles and tasks of a foundational nature? In this talk I will argue that there is still a long way to go in this direction and present recent and ongoing work of ours that aims to lay some of the necessary groundwork for moving forward. In particular, I will present three vignettes, touching on the problems of (i) adjusting for bias inherent in network sampling, (ii) propagation of uncertainty to summary statistics of `noisy' networks, and (iii) estimation and testing for large collections of network data objects. In each case I will present a formalization of a certain class of problems encountered frequently in practice, describe our work in addressing the core aspects of these problems, and point to some of the many outstanding challenges remaining.
Eric Kolaczyk is Professor of Statistics, and Director of the Program in Statistics, in the Department of Mathematics and Statistics at Boston University, where he also is an affiliated faculty member in the Program in Bioinformatics, the Program in Computational Neuroscience, and the Division of Systems Engineering. Prof. Kolaczyk's main research interests currently revolve around the statistical analysis of network-indexed data, and include both the development of basic methodology and inter-disciplinary work with collaborators in bioinformatics, computer science, geography, neuroscience, and sociology. Besides his research articles on these topics, he has also authored two books in this area — Statistical Analysis of Network Data: Methods and Models (Springer, 2009) and Statistical Analysis of Network Data with R (Springer, 2014) -- from which he gives short courses regularly in recent years, including for the Center for Disease Control (CDC) and the Statistical and Applied Mathematical Sciences Institute (SAMSI) in the US as well as similar venues in Belgium, England, and France. Prior to his working in the area of networks, Prof. Kolaczyk spent a decade working on statistical multi-scale modeling. Prof. Kolaczyk has served as associate editor on several journals, including currently the Journal of the American Statistical Association and the new IEEE Transactions on Network Science and Engineering, and previously the IEEE Transactions on Image Processing. He is an elected fellow of the American Statistical Association (ASA), an elected senior member of the Institute for Electrical and Electronics Engineers (IEEE), and an elected member of the International Statistical Institute (ISI).