• 统计研究中心
当前位置: 首页> 系列讲座> 正文

香港城市大学王军辉教授:Network embedding and community detection in directed networks

光华讲坛——社会名流与企业家论坛第 5777 期

Network embedding and community detection in directed networks

主讲人香港城市大学王军辉教授

主持人统计学院林华珍教授

时间2021年5月24日(周一)上午11:00-12:00

直播平台及会议ID腾讯会议,386 661 287

主办单位:统计研究中心和统计学院 科研处

主讲人简介:

Prof. Junhui Wang is Professor in the School of Data Science at City University of Hong Kong. He received his B.S. in Probability and Statistics from Peking University, and Ph.D. in Statistics from University of Minnesota. Before joining CityU, he was faculty member at Columbia University and University of Illinois at Chicago. His research interests include statistical machine learning and its applications in biomedicine, economics, finance and information technology. He has actively published research articles on leading statistics and machine learning journals, including Journal of American Statistical Association, Biometrika, and Journal of Machine Learning Research. He also serves as Associate Editor of Statistica Sinica, Annals of the Institute of Statistical Mathematics, and Statistics and its interface.

王军辉教授现为香港城市大学数据科学学院教授。他本科毕业于北京大学,研究生毕业于美国明尼苏达大学并获得统计学博士学位。在加入香港城市大学之前,王军辉教授曾任教于美国哥伦比亚大学和伊利诺伊大学芝加哥分校。他的研究方向包括统计机器学习及其在生物医学,经济,金融,和信息技术上的应用。他的研究成果广泛发表于Journal of American Statistical Association, Biometrika, Journal of Machine Learning Research等统计及机器学习的顶级期刊,并担任Statistica Sinica, Annals of the Institute of Statistical MathematicsStatistics and its interface等期刊的副主编。

内容提要:

Community detection in network data aims at grouping similar nodes sharing certain characteristics together. Most existing methods focus on detecting communities in undirected networks, where similarity between nodes is measured by their node features and whether they are connected. In this talk, we will introduce a novel method to conduct network embedding and community detection simultaneously in a directed network. The network embedding model introduces two sets of vectors to represent the out- and in-nodes separately, and thus allows the same nodes belong to different outand in-communities. The community detection formulation equips the negative log-likelihood with a novel regularization term to encourage community structure among the nodes representations, and thus achieves better performance by jointly estimating the nodes embeddings and their community structures. The asymptotic properties of the proposed method will be discussed in terms of both network embedding and community detection, which are also supported by numerical experiments on some simulated and real examples.

网络数据中的社区检测旨在将共享某些特征的相似节点分组在一起。现有的大多数方法都专注于检测无向网络中的社区,在这些网络中,节点之间的相似性是通过节点的节点特征以及是否连接来衡量的。在本次演讲中,我们将介绍一种在定向网络中同时进行网络嵌入和社区检测的新颖方法。网络嵌入模型引入了两组向量分别代表节点外和节点内,因此允许相同的节点属于不同的节点外社区。社区检测公式为负对数可能性配备了新颖的正则化术语,以鼓励节点表示之间的社区结构,从而通过联合估计节点嵌入及其社区结构来实现更好的性能。将从网络嵌入和社区检测两个方面讨论所提出方法的渐近性质,同时在一些模拟和真实实例上的数值实验也支持了这种方法的渐近性质。




上一条:加州大学伯克利分校李乐昕教授:Testing Mediation Effects Using Logic of Boolean Matrices with Applications in Neuroimaging Mediation Analysis

下一条:普渡大学王啸教授:Inferential Wasserstein Generative Adversarial Networks