报告人：李挺
报告地点：腾讯会议ID:192-940-675
报告时间：2022年09月30日星期五16:00-17:00
报告摘要：
Networks arise in many areas of research and applications, which come in all shapes and sizes. The most studied and best understood are static network models. Many other network models are also in existence, but have been less studied. One such example is the multi-layer networks, which are a powerful representation of relational data, and commonly encountered in contemporary data analysis. The nodes in a multi-layer network represent the entities of interest and the edges in different layers indicate the multiple relations among those entities. Examples include brain connectivity networks, world trading networks, gene-gene interactive networks and so on. In this talk, we focus on the multi-layer networks with the same nodes set of each layer and there are no edges between two different layers. We introduce two novel methods for three real datasets: worldwide trading networks, malaria parasite genes networks and brain connectivity networks.
主讲人简介：
Dr.Li is an assistant professor in the Department of Applied Mathematics at Hong Kong Polytechnic University. Prior to joining PolyU, he was a postdoctoral associate in Yale University, Biostatistics Department. He received his PhD in Hong Kong University of Science and Technology. His research focuses on data science and statistical learning on complex data, especially on network data, brain data and imaging genomics.
