报告人：潘蕊
报告地点：腾讯会议ID:997-894-020
报告时间：2022年10月27日星期四10:00-11:00
报告摘要：
Collaboration network analysis is a useful tool to study researchers’ collaborations. In this work, we collect papers published between 2001 and 2018 in 43 statistical journals and investigate the collaborative trends and patterns. We construct an attributed collaboration network and extract its core. Community detection is conducted on the core network by using the edge cross-validation (ECV) method and attributed network clustering algorithm (ANCA). Influential researchers are identified in each community. Besides, two kinds of homophily are revealed in our collaboration network: research topic homophily and spatial proximity homophily. Finally, we compare ANCA with the other 3 methods and confirm that the combination of nodal attributes and network structure improves the quality of community detection.
主讲人简介：
潘蕊，中央财经大学统计与数学学院副教授，北京大学光华管理学院经济学博士。中央财经大学龙马学者青年学者。主要研究领域为网络结构数据分析、大规模数据计算等。在Annals of Statistics、Journal of the American Statistical Association、《中国科学：数学》等国内外期刊发表论文20余篇。著有专著《数据思维实践》。主持国家自然科学基金面上和青年项目各1项。
