Paper Info
Title
COAUTHORSHIP AND CITATION NETWORKS FOR STATISTICIANS
Abstract
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. 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 findings are consistent with the belief that the statistics community has become increasingly more collaborative, competitive and globalized. For (b), we have identified the most prolific/collaborative/highly cited authors. We have also identified a handful of "hot" papers, suggesting "Variable Selection" as one of the "hot" areas. For (c), we have identified about 15 meaningful communities or research groups, including large-size ones such as "Spatial Statistics," "Large-Scale Multiple Testing" and "Variable Selection" as well as small-size ones such as "Dimensional Reduction," "Bayes," "Quantile Regression" and "Theoretical Machine Learning." Our findings shed light on research habits, trends and topological patterns of statisticians. The data sets provide a fertile ground for future research on social networks.
Year
DOI
Venue
2014
10.1214/15-AOAS896
ANNALS OF APPLIED STATISTICS
Keywords
DocType
Volume
Adjacent rand index,centrality,collaboration,community detection,Degree Corrected Block Model,productivity,social network,spectral clustering
Journal
10
Issue
ISSN
Citations 
4
1932-6157
8
PageRank 
References 
Authors
0.55
4
2
Name
Order
Citations
PageRank
Pengsheng Ji180.89
Jiashun Jin21147.75