报告题目：Estimating network edge probabilities by neighborhood smoothing

时间：2015年12月3（星期四）13：30-14：30

地点：学院南路校区，学术会堂603（如有变更另行通知）

报告人：Professor Ji Zhu, Department of Statistics, University of Michigan

摘要：

The problem of estimating probabilities of network edges from the observed adjacency matrix has important applications to predicting missing links and network denoising. It has usually been addressed by estimating the graphon, a function that determines the matrix of edge probabilities, but is ill-defined without strong assumptions on the network structure. Here we propose a novel computationally efficient method based on neighborhood smoothing to estimate the expectation of the adjacency matrix directly, without making the strong structural assumptions graphon estimation requires. The neighborhood smoothing method requires little tuning, has a competitive mean-squared error rate, and outperforms many benchmark methods on the task of link prediction in both simulated and real networks.

报告人简介：

朱冀教授为中央财经大学“手拉手”项目特聘教授，美国密歇根大学教授，斯坦福大学统计学博士。美国统计学会等多个学会会员，曾获得美国自然科学基金 CAREER奖。在JASA等顶级统计学期刊发表学术论文60余篇。同时担任Journal of the American Statistical Association等多个国际著名统计学期刊的副主编。