好色视频 学术报告[2026]087号

（高水平大学建设系列报告1346号）

报告题目：Influence Maximization via Information Cascade Embedding

报告人：王军辉 教授 （香港中文大学）

报告时间：2026年9月12日16:40-17:30

报告地点：粤海校区汇研楼601会议室

报告摘要：Identifying influential users from information cascades is a central task in network science, yet it is often complicated by unobserved network structure. In this talk, we ppropose a cascade embedding model that embeds each user into a latent space and treats the diffusion process as a self-activated survival model without requiring an observed network. Particularly, the hazard function is specified via the heat kernel form, capturing the physical phenomenon of heat diffusion’s rise-to-peak and decay dynamics. The proposed model is identifiable up to rigid motions of the embedding, including translation, rotation, and reflection. Asymptotic properties are established based on the geometry of the quotient space through maximizing likelihood.

Building upon the fitted embedding, we formulate the classical multiple-seed influence maximization problem, establish monotonicity and submodularity of the expected spread, and quantify how embedding estimation and Monte Carlo errors affect greedy seed selection. Its effectiveness is further demonstrated through extensive simulations and an application to meme diffusion across websites.

报告人简介：王军辉教授现为香港中文大学统计系教授兼系主任。他本科毕业于北京大学，研究生毕业于美国明尼苏达大学并获得统计学博士学位。他是国际数理统计学会会士(IMS Fellow)和国际统计学会当选会员(ISI Elected Member)。他的研究方向包括统计机器学习及其在生物医学，经济金融，和信息技术上的应用。他的研究成果广泛发表于JASA, Biometrika, JMLR和NeurIPS等统计及机器学习的顶级期刊和会议，并担任JASA，AoAS, JCGS, Statistica Sinica等主流期刊的副主编。

邀请人：周彦

好色视频

2026年9月9日