[Dmbu-l] talk by Yizhou Sun, Northeastern -- Friday, 9/19 10:30 in MCS 137

Dora Erdos edori at bu.edu
Fri Sep 12 13:43:55 EDT 2014

Hi All,

next week's speaker is Yishou Sun from Northeastern. Please note that 
for this one time we are in MCS 137.

Please let me know if you would like to meet with the speaker and what 
times are good for you. You can also team up with someone and have a 
meeting with Yizhou together.


Title: Mining Heterogeneous Information Networks

Real-world physical and abstract data objects are interconnected, forming 
gigantic, interconnected networks.  By structuring these data objects and 
interactions between these objects into multiple types, such networks 
become semi-structured heterogeneous information networks. Most real-world 
applications that handle big data, including interconnected social media 
and social networks, scientific, engineering, or medical information 
systems, online e-commerce systems, and most database systems, can be 
structured into heterogeneous information networks.

Different from homogeneous information networks, where objects and links 
are treated either as of the same type or as of untyped nodes or links, 
heterogeneous information networks in our model are semi-structured and 
typed, following a network schema. We then propose different methodologies 
in mining heterogeneous information networks by carefully modeling the 
links from different types. In this talk, I will introduce three recent 
developed techniques, which include (1) meta-path-based mining, (2) 
relation strength-aware mining, and (3) semantic-aware relation modeling, 
and their applications, such as similarity search, clustering, information 
diffusion, and voting prediction.

Yizhou Sun is 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, database systems, statistics, 
machine learning, information retrieval, 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 based on her thesis 
work on mining heterogeneous information networks have been given in 
several premier conferences, including EDBT 2009, SIGMOD 2010, KDD 2010, 
ICDE 2012, VLDB 2012, and ASONAM 2012.  She received 2012 ACM SIGKDD Best 
Student Paper Award, 2013 ACM SIGKDD Doctoral Dissertation Award, and 2013 
Yahoo ACE (Academic Career Enhancement) Award.

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