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Title: Coauthorship and citation networks for statisticians Authors:  Jiashun Jin - Carnegie Mellon University (United States) [presenting]
Abstract: A data set has been collected for the networks of statisticians, consisting of titles, authors, abstracts, MSC numbers, keywords, and citation counts of papers published in representative journals, for $36$ journals in statistics and related fields, spanning about $35$ years. The data set provides a fertile ground for research in social networks, text mining, and knowledge discovery, and motivates an array of interesting problem in statistics and machine learning. We provide an expository overview on this data set, and discuss several problems including overall productivity of statisticians, statistical journal ranking, citation patterns, co-authorship network communities, co-authorship network mixed-memberships, dynamic networks, and topic estimation.