讲座信息

讲座信息

您当前的位置: 讲座信息
20210316刘秉辉:基于轮廓伪似然方法的快速网络社区发现
时间:2021-03-12

报告时间:2021年3月16日星期二 下午14:30

报告嘉宾:刘秉辉

报告主题:基于轮廓伪似然方法的快速网络社区发现


报告摘要

The stochastic block model is one of the most studied network models for community detection. Most algorithms proposed for fitting the stochastic block model likelihood function cannot scale to large-scale networks. One prominent work that overcomes this computational challenge is Amini et al. (2013), which proposed a fast pseudo-likelihood approach for fitting stochastic block models to large sparse networks. However, this approach does not have convergence guarantee, and is not well suited for small- or medium- scale networks. In this article, we propose a novel likelihood based approach that decouples row and column labels in the likelihood function, which enables a fast alternating maximization; the new method is computationally efficient, performs well for both small and large scale networks, and has provable convergence guarantee.


个人简介

刘秉辉,东北师范大学,教授、博导,统计系主任;毕业于东北师范大学,师从郭建华教授;曾到美国明尼苏达大学进行博士后访问,合作导师是沈晓彤教授和潘伟教授。主要研究方向为统计学习和网络数据分析,在Artificial Intelligence、Journal of Machine Learning Research、Annals of Applied Statistics、Journal of Business & Economic Statistics、Statistics in Medicine等期刊发表多篇学术论文;主持国家自然科学基金面上项目、青年项目、省级重点教改项目等;与中国联通公司合作,主持大数据培训、大数据分析项目若干。



扫描下方二维码报名↘

所有消息会在两个群中同步通知

请大家不要重复加群~