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2017年6月12日14:00 Hanghang Tong@亚利桑那州立大学:Inside the Atoms: Mining a Network of Networks and Beyond

来源: 计算机学院 | 发表时间: 2017-06-08 | 浏览次数: 107


报告题目:Inside the Atoms: Mining a Network of Networks and Beyond

报告人:Prof. Hanghang Tong

Data Lab@ASU

Arizona StateUniversity, Tempe, Arizona, USA

时间:     20176121400

地点:南京邮电大学仙林校区计算机学科楼327

主办单位:研究生院、计算机学院、软件学院、网络空间安全学院(大数据研究院)、江苏省人工智能学会数据挖掘与应用专委会、江苏省大数据安全与智能处理重点实验室

告人简介:Hanghang Tong is currently an assistant professor at School of Computing, Informatics, and Decision Systems Engineering (CIDSE), Arizona State University since August 2014. Before that, he was an assistant professor at Computer Science Department, City College, City University of New York, a research staff member at IBM T.J. Watson Research Center and a Post-doctoral fellow in Carnegie Mellon University. He received his M.Sc and Ph.D. degree from Carnegie Mellon University in 2008 and 2009, both majored in Machine Learning. His research interest is in large scale data mining for graphs and multimedia. He has received several awards, including one test of time award (ICDM 10-Year highest impact paper award), NSF CAREER award (2017), four best paper awards, five best of conference and a best demo honorable mention (SIGMOD 2017). He has published over 100 referred articles and more than 20 patents.

  

报告内容:Networks (i.e., graphs) appears in many high-impact applications. Often these networks are collected from different sources, at different times, at different granularities. In this talk, I will present our recent work on mining such multiple networks. First, we will present two models - one on modeling a set of inter-connected networks (NoN); and the other on modeling a set of inter-connected co-evolving time series (NoT). For both models, we will show that by treating networks as context, we are able to model more complicate real-world applications. Second, we will present some algorithmic examples on how to do mining with such new models, including ranking, imputation and prediction. Finally, we will demonstrate the effectiveness of our new models and algorithms in some applications, including bioinformatics, and sensor networks.