Dr. Chuan Shi: Heterogeneous information network embedding: from static to dynamic

Presenter: Chuan Shi

Abstract:

Most real systems consist of a large number of interacting, multi-typed components, which can be modeled as heterogeneous information networks. Recently, heterogeneous information network has attracted more and more attentions, since it contains rich structure and semantic information through fusing heterogeneous information. Meanwhile, with the surge of deep learning, network embedding has shown its powerful ability to learn the feature representation of nodes for downstream tasks. However, contemporary network embeddings focus on homogeneous network. In this talk, I will introduce our recent work on heterogeneous information network embedding. Moreover, some newest work on dynamic heterogeneous network embedding will also be discussed. 

Bio:

Chuan Shi is the professor in School of Computer Sciences of Beijing University of Posts and Telecommunications, deputy director of Beijing Key Lab of Intelligent Telecommunication Software and Multimedia. The main research interests include data mining, machine learning, artificial intelligence and big data analysis. He has published more than 100 refereed papers, including top journals and conferences in data mining, such as IEEE TKDE, ACM TIST, KDD, AAAI, IJCAI, and WWW. And in the meanwhile, his first monograph about heterogeneous information networks has been published by Springer. He has been honored as the best paper award in ADMA 2011 and ADMA 2018, and has guided students to the world champion in the IJCAI Contest 2015, the premier international data mining competition. He is also the recipient of “the Youth Talent Plan” and “the Pioneer of Teacher's Ethics” in Beijing.