12月18日：Dr. Peng Cui: Network Embedding: Enabling Network Analytics and Inference in Vector Space

报告题目: Network Embedding: Enabling Network Analytics and Inference in Vector Space
报告人：崔鹏 副教授，清华大学
主持人：严骏驰
邀请人：查宏远
报告时间：2017年12月18日10:00-11:30
报告地点：中北校区 数学馆201报告厅

报告人简介：
Peng Cui is an associate professor in Tsinghua University. He got his PhD degree from Tsinghua University in 2010. He is keen to promote the convergence of social media data mining and multimedia computing technologies. His research interests include network representation learning, human behavioral modeling, and social-sensed multimedia computing. He has published more than 60 papers in prestigious conferences and journals in data mining and multimedia. His recent research won the SIGKDD 2016 Best Paper Finalist, ICDM 2015 Best Student Paper Award, SIGKDD 2014 Best Paper Finalist, IEEE ICME 2014 Best Paper Award, ACM MM12 Grand Challenge Multimodal Award, and MMM13 Best Paper Award. He is the Area Chair of ICDM 2016, ACM MM 2014-2018, IEEE ICME 2014-2015, ICASSP 2013, Associate Editors of IEEE TKDE, ACM TOMM, Elsevier Journal on Neurocomputing, and Guest Editors of IEEE Intelligent Systems, Information Retrieval Journal, Machine Vision and Applications, etc. He was the recipient of ACM China Rising Star Award in 2015.

报告摘要:
Nowadays, larger and larger networks are used in applications. It is well recognized that network data is sophisticated and challenging. To process graph data effectively, the first critical challenge is network data representation, that is, how to represent networks properly so that advanced analytic tasks, such as pattern discovery, analysis and prediction, can be conducted efficiently in both time and space. In this talk, we will review the recent thoughts and achievements on network embedding. More specifically, a series of fundamental problems in network embedding will be discussed, including why we need to revisit network representation, what are the research goals of network embedding, how network embedding can be learned, and the major future directions of network embedding.