报告时间：2018年7月24日（星期二）上午10：00
报告地点：浙江大学紫金港校区蒙民伟楼402会议室
报告人：Yizhou Sun 助理教授
主持人：蔡登教授
Title: Embedding Approaches for Mining Heterogeneous Information Networks
Abstract:One of the challenges in mining information networks is the lack of intrinsic metric in representing nodes into a low dimensional space, which is essential in many mining tasks, such as anomaly detection, link prediction, and recommendation. Moreover, when coming to heterogeneous information networks, where nodes belong to different types and links represent different semantic meanings, it is even more challenging to represent nodes properly for a particular task. In this talk, I will introduce our recent progress of network embedding approaches that are designed for heterogeneous information networks, and discuss (1) how network embedding can be designed in unsupervised tasks, such as anomaly detection; (2) how to learn network embeddings when guidance is available, such as link prediction; and (3) how to design more complex embedding function when rich content information is available for nodes, such as recommendation. Our results on several application domains, including enterprise networking, social network, bibliographic data, and biomedical data, have demonstrated the superiority as well as the interpretability of these new methodologies.
Bio: Yizhou Sun is an assistant professor at department of computer science of UCLA. Prior to that, she was an assistant professor in the College of Computer and Information Science of Northeastern University. She received her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2012. Her principal research interest is in mining information and social networks, and more generally in data mining, machine learning, and network science, with a focus on modeling novel problems and proposing scalable algorithms for large-scale, real-world applications. She is a pioneer researcher in mining heterogeneous information network. Yizhou has over 80 publications in books, journals, and major conferences. Tutorials on mining heterogeneous information networks have been given in several premier conferences, including EDBT 2009, SIGMOD 2010, KDD 2010, ICDE 2012, VLDB 2012, ASONAM 2012, ACL 2015, WWW’17 and KDD’17. She received 2012 ACM SIGKDD Best Student Paper Award, 2013 ACM SIGKDD Doctoral Dissertation Award, 2013 Yahoo ACE (Academic Career Enhancement) Award, 2015 NSF CAREER Award, and 2016 CS@ILLINOIS Distinguished Educator Award.
