报告题目：Graph Neural Network: An Introduction and Some Recent Works
报告人：周川，中国科学院数学与系统科学研究院副研究员
报告时间：2021 年 10 月 29 日（星期五）下午 18: 00
腾讯会议：815 741 454
报告摘要: Learning with graph structured data, such as social, biological, and financial networks, requires effective representation of their graph structure. Recently, there has been a surge of interest in Graph Neural Network (GNN) approaches for graph representation learning. GNN generalizes neural network (CNN) from low-dimensional regular grids, where image, video and speech are represented, to graph structure data. To date, GNN has been successfully applied to many noteworthy applications, such as node classification, link prediction, recommendation and traffic prediction. This speech will mainly review the GNN with the background, emerging challenges, basic concepts, state-of-the-art algorithms, and some of our recent works.
报告人简介：周川，现为中国科学院数学与系统科学研究院副研究员，博士生导师。研究方向为社会计算、社交网络分析、图挖掘、统计机器学习等，在国际顶级学术期刊和会议（如TKDE、DMKD、PR、AAAI、IJCAI、ICDM、CIKM等）上累计发表学术论文60余篇。承担国家自然科学基金、国家重点研发计划等10余项科研课题。获得中科院数学院陈景润未来之星、CCF Senior Member(即中国计算机学会高级会员)等荣誉。