报告题目：A multi-layer academic network dataset derived from statistical publications

报告时间：2024年5月24日 上午9：30

报告地点：南湖校区老图书馆四楼会议室

主办单位：数学与统计学院

报告人：潘蕊

报告人简介：潘蕊，中央财经大学统计与数学学院教授、博士生导师，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的统计建模、时空数据的统计分析等。在Annals of Statistics、Journal of the American Statistical Association、Journal of Business & Economic Statistics等期刊发表论文30余篇。著有中文专著《数据思维实践》、《网络结构数据分析与应用》。主持国家自然科学基金项目。具有丰富的案例创作和授课经验，曾获得中央财经大学青年教师教学基本功比赛二等奖，首届中国高校财经慕课联盟“同课异构”课程思政教学竞赛一等奖。

摘要：The utilization of multi-layer network structures now enables the explanation of complex systems in nature from multiple perspectives. Multi-layer academic networks capture diverse relationships among academic entities, facilitating the study of academic development and the prediction of future directions. However, there are currently few academic network datasets that simultaneously consider multi-layer academic networks; often, they only include a single layer. In this study, we provide a large-scale multi-layer academic network dataset, namely, LMANStat, which includes collaboration, co-institution, citation, co-citation, journal citation, author citation, author-paper and keyword co-occurrence networks. Furthermore, each layer of the multi-layer academic network is dynamic. Additionally, we expand the attributes of nodes, such as authors' research interests, productivity, region and institution. Supported by this dataset, it is possible to study the development and evolution of statistical disciplines from multiple perspectives. This dataset also provides fertile ground for studying complex systems with multi-layer structures.