EECS500 Fall 2014 Department Colloquium

Jiawei Han
Taming Interconnect and Unstructured Data: Construction and Mining of Heterogeneous Information Networks
University of Illinois
White 411
November 13, 2014

Massive amounts of data are unstructured, noisy, untrustworthy, but are interconnected, forming gigantic, interconnected information networks.  By structuring such unstructured data 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, medical information systems, online e-commerce systems, or Web-based database systems, can be structured into typed, heterogeneous social and information networks.  For example, in a medical care network, objects of multiple types, such as patients, doctors, diseases, medication, and links such as visits, diagnosis, and treatments are intertwined together, providing rich information and forming heterogeneous information networks.  Effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge.

In this talk, we present a set of scenarios for construction and mining of heterogeneous information networks.  We show that relatively structured heterogeneous information networks can be constructed from unstructured, interconnected data, and such relatively structured, heterogeneous networks brings tremendous benefits for data management and data mining.  Departing from many existing network models that view data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and can uncover surprisingly rich knowledge from interconnected data.  This heterogeneous network modeling will lead to the discovery of a set of new principles and methodologies for mining interconnected data. We will also point out some promising research directions and provide convincing arguments on that construction and mining of heterogeneous information networks could be a key to Web-aged information management and mining.


Jiawei Han, Abel Bliss Professor of Computer Science, University of Illinois at Urbana-Champaign.  He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 600 journal and conference publications. He has chaired or served on many program committees of international conferences, including PC co-chair for KDD, SDM, and ICDM conferences, and Americas Coordinator for VLDB conferences. He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and is serving as the Director of Information Network Academic Research Center supported by U.S. Army Research Lab. He is a Fellow of ACM and IEEE, and received 2004 ACM SIGKDD Innovations Award, 2005 IEEE Computer Society Technical Achievement Award, 2009 IEEE Computer Society Wallace McDowell Award, and 2011 Daniel C. Drucker Eminent Faculty Award at UIUC.  His book "Data Mining: Concepts and Techniques" has been used popularly as a textbook worldwide.