报告时间：2023年5月30日

               上午10:00

报告地点：中国人民大学明德主楼1016

（腾讯会议ID：638 654 641）

报告嘉宾：王天栋

报告主题：随机网络中的互易性和极值依赖性

报告摘要

Reciprocity and Large Degree Dependence in Random Networks

Users of social networks display diversified behavior and online habits. For instance, a user's tendency to reply to a post can depend on both the user and the person posting. For convenience, we group users into aggregated behavioral patterns, focusing here on the tendency to reply or reciprocate to a message. The reciprocity feature in social networks reflects the information exchange among users. We study properties of a preferential attachment model with heterogeneous reciprocity levels, and give the growth rate of model edge counts as well as prove convergence of empirical degree frequencies to a limiting distribution. This limiting distribution is not only multivariate regularly varying, but also has the property of hidden regular variation.

作者简介

王天栋，上海数学中心青年研究员，博士生导师。2013年毕业于澳大利亚国立大学精算专业，取得荣誉学士学位；2019年毕业于美国康奈尔大学运筹学与信息工程学院，取得博士学位；2019年9月至2022年8月任职于美国得州农工大学统计系，担任助理教授。2022年9月至今任职于上海数学中心，担任青年研究员。2017年以来在Stochastic Processes and Their Applications, Extremes, Statistical Computing等国际学术期刊上发表高水平论文13篇。