报告人：卿欢

报告时间：2023/5/10 10:00-11:00（GMT+08：00)

报告地点：数学中心 学术研讨室（至善楼602）

腾讯会议：403-747-949

Abstract：

    Modeling and estimating mixed memberships for overlapping unipartite un-weighted networks has been well studied in recent years. However, to our knowledge, there is no model for a more general case, the overlapping bipartite weighted networks. To close this gap, we introduce a novel model, the Bipartite Mixed Membership Distribution-Free (BiMMDF) model. Our model allows an adjacency matrix to follow any distribution as long as its expectation has a block structure related to node membership. In particular, BiMMDF can model overlapping bipartite signed networks and it is an extension of many previous models, including the popular mixed membership stochastic blcokmodels. An efficient algorithm with a theoretical guarantee of consistent estimation is applied to fit BiMMDF. We also consider missing edges for sparse networks. The advantage of BiMMDF is demonstrated in extensive synthetic networks and several real-world networks.

个人简介：

       卿欢是中国矿业大学数学学院讲师。2015年毕业于北京师范大学数学科学学院。2020年于新加坡国立大学获得数学博士学位，师从童心教授。主要研究方向是复杂网络下的网络聚类。