报告题目:迈向最优深度图学习

报告时间:2023年6月29日（周四） 14:30-16:05

会议地点:九里校区1243会议室

主讲人:主讲人:Hanghang Tong 副教授

报告简介:

Hanghang Tong目前是伊利诺伊大学香槟分校计算机科学系的副教授。在此之前，他曾任亚利桑那州立大学计算机、信息学和决策系统工程学院（CIDSE）的副教授。他于2008年和2009年在卡内基梅隆大学获得机器学习的硕士和博士学位，研究方向主要集中在大规模图数据挖掘和多媒体方面。他获得过多项奖项，包括ACM杰出成员（2020年）、ICDM Tao Li奖（2019年）、SDM/IBM早期职业数据挖掘研究奖（2018年）、NSF CAREER奖（2017年）、ICDM十年最高影响论文奖（2015年、2022年）以及多个最佳论文奖，还担任过ACM SIGKDD Explorations的主编，并且是IEEE的会士。

Hanghang Tong is currently an associate professor at Department of Computer Science at University of Illinois at Urbana-Champaign. Before that he was an associate professor at School of Computing, Informatics, and Decision Systems Engineering (CIDSE), Arizona State University. He received his M.Sc. and Ph.D. degrees from Carnegie Mellon University in 2008 and 2009, both in Machine Learning. His research interest is in large scale data mining for graphs and multimedia. He has received several awards, including ACM distinguished member (2020), ICDM Tao Li award (2019), SDM/IBM Early Career Data Mining Research award (2018), NSF CAREER award (2017), ICDM 10-Year Highest Impact Paper award (2015, 2022), and several best paper awards. He was the Editor-in-Chief of SIGKDD Explorations (ACM) and is a fellow of IEEE.

主讲人简介:

The emergence of deep learning models designed for graph and network data, often under an umbrella term named graph neural networks (GNNs for short), has largely streamlined many graph learning problems. In the vast majority of the existing works, they aim to answer the following question, that is, given a graph, what is the best GNNs model to learn from it? In this talk, we introduce the graph sanitation problem, to answer an orthogonal question. That is, given a mining task and an initial graph, what is the best way to improve the initially provided graph? We formulate the graph sanitation problem as a bilevel optimization problem, and further instantiate it by semi-supervised node classification, together with an effective solver named Gasoline.