Coauthorship and Citation Networks for Statisticians

This talk will discuss the recent comment, written jointly with Vishesh Karwa, on the paper with the same title by Ji and Jin. Ji and Jin collected, cleaned and summarized in various ways citation and coauthorship networks for statisticians. They performed several descriptive analyses of the underlying networks to extract interesting patterns: they studied trends of productivity over time, extracted most prolific authors and research areas using various centrality measures, and found communities in these networks. Petrovic and Karwa take a model-based approach and consider the effects of various types of author interactions on the analysis and inference about the citation and coauthorship datasets. This dataset motivates the development of new models for coauthorship data. The talk will give an overview of the data and the kids of questions we (as statisticians) should be asking.

Event Topic

Nonlinear Algebra and Statistics (NLASTATS)