报告题目： Analyze Time-to-event Data Using Survival Mixed Membership Blockmodel （生存混合成员模型分析社交网络中事件发生时间）

报 告 人：宋方达教授 香港中文大学（深圳）分校

报告时间：2024年6月17日（星期一） 10:15-11:15

报告地点：数学楼115（大报告厅）

校内联系人：李长城教授     联系电话：54708354

报告摘要：Whenever we send a message via a channel such as E-mail, Facebook, WhatsApp, WeChat, or LinkedIn, we care about the response rate—the probability that our message will receive a response—and the response time—how long it will take to receive a reply. Recent studies have made considerable efforts to model the sending behaviors of messages in social networks with point processes. However, statistical research on modeling response rates and response times on social networks is still lacking. Compared with sending behaviors, which are often determined by the sender’s characteristics, response rates and response times further depend on the relationship between the sender and the receiver. Here, we develop a survival mixed membership blockmodel (SMMB) that integrates semiparametric cure rate models with a mixed membership stochastic blockmodel to analyze time-to-event data observed for node pairs in a social network, and we are able to prove its model identifiability without the pure node assumption. We develop a Markov chain Monte Carlo algorithm to conduct posterior inference and select the number of social clusters in the network according to the conditional deviance information criterion. The application of the SMMB to the Enron E-mail corpus offers novel insights into the company’s organization and power relations.

报告人简介：宋方达博士，香港中文大学（深圳）数据科学学院助理教授，博士生导师。2016年本科毕业于浙江大学统计学专业，2020年获得香港中文大学统计学博士学位，师承魏颖颖教授。主要研究兴趣为贝叶斯统计统计建模，模型可识别性和渐进性证明，高效统计推断算法开发，聚焦前沿科学领域中的数据分析，包括基因组学、社交网络、在线学习、地质勘测等领域。2019年获美国统计协会统计遗传学和基因组学优秀学生论文奖。相关工作发表于JASA，Nature Communications等期刊，主持国家自然科学基金青年基金项目资助一项。