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20180919 王汉生:A Popularity Scaled Latent Space Model for Large-Scale Directed Social Network
时间:2018-09-19

时间:9月19日(周三) 14:30

地点:明德主楼 1016 会议室

题目:A Popularity Scaled Latent Space Model for Large-Scale Directed Social Network


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.


  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,嘉茂荣聘讲席教授,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)。在理论研究方面,主要关注变量选择、数据降维、高维数据分析、以及复杂网络数据分析。所有这些研究都以大规模、复杂、超高维数据分析为核心。其相关的应用领域包括但不局限于:中文文本、网络结构、位置轨迹。在业界实践方面,王汉生教授是国内较早从统计数据分析角度关注并研究搜索引擎营销,社交网络数据,以及位置轨迹数据分析的学者。曾担任博雅立方科技有限公司首席科学(2009—2015),百分点首席统计学家(2015—现在)。此外,量帮科技、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、奥迪、京东、联通等众多企业有短期项目、或者培训会议合作。