Jiawei Han

University of Illinois at Urbana-Champaign

Jihawi Han

Jiawei Han is a Professor in the Department of Computer Science at the University of Illinois. He has been working on research into data mining, data warehousing, stream data mining, spatiotemporal and multimedia data mining, information network analysis, text and Web mining, and software bug mining, with over 400 conference and journal publications. He has chaired or served in over 100 program committees of international conferences and workshops and also served or is serving on the editorial boards for Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Journal of Computer Science and Technology, and Journal of Intelligent Information Systems. He is currently the founding Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data (TKDD). Jiawei has received IBM Faculty Awards, the Outstanding Contribution Award at the International Conference on Data Mining (2002), ACM SIGKDD Innovation Award (2004), and IEEE Computer Society Technical Achievement Award (2005). He is a Fellow of ACM and IEEE. His book "Data Mining: Concepts and Techniques" (Morgan Kaufmann) has been used worldwide as a textbook.

Mining Heterogeneous Information Networks By Exploring the Power of Links

Many objects in the real world are interconnected, forming complex information networks. There have been a lot of studies on mining homogeneous information networks where objects and links are either treated as of the same type, such as friends linking with friends, or treated indiscriminatively, without structural or type distinction. However, real-world objects and links often belong to distinct types, such as students, professors, courses, departments, teach and advise in a university network, and such typed networks form structured, heterogeneous information networks.

We explore methodologies on mining such structured information networks and introduce several interesting new mining methodologies, including integrated ranking and clustering, classification, role discovery, data integration, data validation, and similarity search. We show that structured information networks are informative, and link analysis on such networks becomes powerful at uncovering critical knowledge hidden in large networks.


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