Coauthorship and Citation Networks for Statisticians

We have collected and cleaned two network data sets: Coauthorship and Citation networks for statisticians. The data sets are based on all research papers published in four of the top journals in statistics from 2003 to the first half of 2012. We analyze the data sets from many different perspectives, focusing on (a) productivity, patterns and trends, (b) centrality, and (c) community structures, and present an array of interesting findings. For (a), we find that over the 10-year period, both the average number of papers per author and the fraction of self citations have been decreasing, but the proportion of distant citations has been increasing. These suggest that the statistics community has become increasingly more collaborative, competitive, and globalized, driven by the boom of online resources and Search Engines. For (b), we identify the most prolific, the most collaborative, and the most highly cited authors. We also identify a handful of “hot” papers and suggest “Variable Selection” and several other areas as the “hot” areas in statistics. For (c), we have investigated the data sets with 6 different methods, including D-SCORE (a new procedure we propose here). We have identified about 15 meaningful communities, including large-size communities such as “Biostatistics”, “Large-Scale Multiple Testing”, “Variable Selection” as well as small-size communities such as “Dimensional Reduction”, “Objective Bayes”, “Quantile Regression”, and “Theoretical Machine Learning”. We find that the community structures for the Coauthorship network and Citation network are connected and intertwined, but are also different in significant ways. Our findings shed light on research habits, trends, and topological patterns of statisticians, and our data sets provide a fertile ground for future researches on or related to social networks of statisticians.