报告简介  Abstract

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 out- and 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.

嘉宾简介  About the Speaker

Dr. 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 serves as Associate Editor of StatisticaSinica, Annals of the Institute of Statistical Mathematics, and Statistics and its interface.

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