Mining Heterogeneous Information Networks: Yizhou Sun, Northeastern (Data Management Seminar)

  • Starts: 10:15 am on Friday, September 19, 2014
  • Ends: 11:30 am on Friday, September 19, 2014
Abstract: 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. Bio: 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.
MCS 137