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We have collected and cleaned two network data sets: Coauthorship andCitation networks for statisticians. The data sets are based on all researchpapers published in four of the top journals in statistics from $2003$ to thefirst half of $2012$. We analyze the data sets from many differentperspectives, focusing on (a) centrality, (b) community structures, and (c)productivity, patterns and trends.  For (a), we have identified the most prolific/collaborative/highly citedauthors. We have also identified a handful of "hot" papers, suggesting"Variable Selection" as one of the "hot" areas.  For (b), we have identified about $15$ meaningful communities or researchgroups, including large-size ones such as "Spatial Statistics", "Large-ScaleMultiple Testing", "Variable Selection" as well as small-size ones such as"Dimensional Reduction", "Objective Bayes", "Quantile Regression", and"Theoretical Machine Learning".  For (c), we find that over the 10-year period, both the average number ofpapers per author and the fraction of self citations have been decreasing, butthe proportion of distant citations has been increasing. These suggest that thestatistics community has become increasingly more collaborative, competitive,and globalized.  Our findings shed light on research habits, trends, and topological patternsof statisticians. The data sets provide a fertile ground for future researcheson or related to social networks of statisticians.

Coauthorship and Citation Networks for Statisticians