报告题目:Efficient Split Likelihood Method for Community Detection of Large-scale Networks
主讲人:刘秉辉
报告摘要: To recover community labels under the stochastic block model (SBM), we propose a split likelihood (SL) framework, which aims at providing a rapidly converging algorithm with advantages in terms of both the accuracy of community detection and computational efficiency. Under such framework, we create an alternative inference function, the split likelihood, to avoid handling the problem of the intractability of the inference of the likelihood of the original observation, by splitting variables of the original SBM into two independent split bodies with identical distribution. Then, we create some effective computing strategies to maximize the split likelihood. Based on them, we propose the efficient SL algorithm and establish its computational and statistical properties. We demonstrate the superiority of the proposed methods via some numerical results as well as a real data analysis.刘秉辉简介:东北师范大学,教授、博导,统计系主任;主要研究方向为应用统计和机器学习;在Artificial Intelligence、Journal of Machine Learning Research、The 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青年项目、面上项目各一项,参与国家自然科学基金重点项目-项;与中国联通公司合作主持大数据分析项目一项、大数据培训项目一项.
报告时间: 2020年11月23日下午3:30-6:00
报告地点:南湖校区教学科研楼405室
主办单位:科研处/数学与统计学院
