Statistical models and algorithms for analysing graphs and node attributes
Invited talk by Edoardo M. Airoldi, Department of Statistics, Harvard University, USA.
Chair: Elena Erosheva, Thursday 23rd July, 14.00 - 14.45, Uppercroft, School of Pythagoras.
Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of graphs and node attributes have emerged as a major topic of interest in diverse areas of study, and most of these involve a collections of measurements on pairs of objects along with object-specific covariates. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active “network community” and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online “networking communities” such as Facebook and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. In this talk, I will review a few ideas that are central to this burgeoning literature. I will emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. I will conclude by describing open problems and challenges for statistical and computational analyses of graphs and node attributes.