报告人：王汉生
报告地点：数学与统计学院415室
报告时间：2018年11月16日星期五10:00-11:00
报告摘要：
Large-scale directed social network data often involve degree heterogeneity, reciprocity, and transitivity properties. A sensible network generating model should take these features into consideration. To this end, we propose a popularity scaled latent space model for the large-scale directed network structure formulation.
It assumes for each node a position in a hypothetically assumed latent space. Then, the nodes close (far away) to each other should have larger (less) probability to be connected. As a consequence, the reciprocity and transitivity properties can be analytically derived.
In addition to that, we assume for each node a popularity parameter. Those nodes with larger (smaller) popularity are more (less) likely to be followed by other nodes. By assuming different distributions for popularity parameters, different types of degree heterogeneity can be modeled. Furthermore, based on the proposed model, a comprehensive probabilistic index is constructed for link prediction. Its finite sample performance is demonstrated by extensive simulation studies and a Sina Weibo (a Twitter-type social network in China) dataset. The performances are competitive.
主讲人简介：
王汉生教授，北京大学光华管理学院商务统计与经济计量系，嘉茂荣聘（2014-2015）教授，蓝天环保讲席教授（2015-2016）博导，系主任。北京大学商务智能研究中心主任。微信公众号“狗熊会”创始人。美国统计学会Fellow （2014），国家杰出青年基金获得者（2016）。 在理论研究和统计建模方面，主要关注同移动互联网以及量化投资相关的数据分析。具体内容包括但不局限于：中文文本、网络结构、位置轨迹。在业界实践方面，王汉生教授曾担任博雅立方科技有限公司首席科学家（2009—2015），百分点首席统计学家（2015—现在）。此外，量邦科技、考拉征信、彩虹无线、蓬景数字等众多企业有深度学术合作。涉及量化投资、互联网征信、车联网、移动设备RTB广告竞价、搜索引擎营销、电子商务等多个重要行业。此外，王汉生教授同腾讯、百度、阿里、奇虎360、奥迪、京东、联通等众多企业有短期项目，或者培训会议合作。
