COAUTHORSHIP AND CITATION NETWORKS FOR STATISTICIANS

• 成果类型: Article
• 署名作者: Ji, Pengsheng; Jin, Jiashun
• 署名单位: University System of Georgia; University of Georgia; Carnegie Mellon University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/15-AOAS896
• 发表日期: 2016
• 页码: 1779-1812
• 关键词: nonconcave penalized likelihood; community detection; variable selection; regularization; centrality; graphs; models

摘要

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.