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加利福尼亚大学洛杉矶分校孙怡舟教授学术报告

ag视讯:2016-07-07 编辑: 来源:

Time: 14:00-15:30, July 7, 2016

Venue: Room 310, Office Building, Software Campus

 

Title: Node Representation in Mining Heterogeneous Information Networks

Speaker: Yizhou Sun, University of California at Los Angeles

 

Host: Zhaohui Peng

 

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 recommendation and anomaly detection. 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. In this talk, we will focus on two mining tasks, i.e., (1) content-based recommendation and (2) anomaly detection in heterogeneous categorical events, and introduce (1) how to represent nodes when different types of nodes and links are involved; and (2) how heterogeneous links play different roles in these tasks. Our results have demonstrated the superiority as well as the interpretability of these new methodologies.

 

Bio:

Yizhou Sun is joining department of computer science of UCLA as an assistant professor. 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. Yizhou has over 60 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, and ACL 2015.  She received 2012 ACM SIGKDD Best Student Paper Award,

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