報告題目：Efficient Split Likelihood Method for Community Detection of Large-scale Networks
報 告 人：劉秉輝 副教授 東北師範大學
報告時間：2019年12月10日上午9：00-10：00
報告地點：數學樓第二報告廳
報告摘要：
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.
報告人簡介：
劉秉輝，東北師範大學，副教授，博士生導師，統計系主任；主要研究方向為應用統計、機器學習、網絡數據分析；在Artificial Intelligence、Journal of Machine Learning Research、The Annals of Applied Statistics、Statistics in Medicine、Computational Statistics & Data Analysis等期刊發表多篇學術論文；主持國家自然科學基金青年項目一項、面上項目一項，主持中央高校基本科研業務費青年拔尖人才項目一項，主持吉林省科技廳重點實驗室專項課題一項；與中國聯通合作開發大數據産品一項、主持大數據培訓項目一項；主持開發長春市市長公開電話數據挖掘項目熱點分析子模塊。
