Mining Heterogeneous Information Networks By Exploring the Power of Links
(invited lecture for DS 2009)

Author: Jiawei Han

Affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign, USA

Abstract. Knowledge is power but for interrelated data, knowledge is often hidden in massive links in heterogeneous information networks. We explore the power of links at mining information networks in several interesting tasks including link-based information integration, truth validation, clustering, and integrated clustering and ranking.

Some recent results of our research that explore the crucial information hidden in links will be introduced, including (1) object distinction analysis, (2) veracity analysis, and (3) RankClus: integrated clustering and ranking. We also discuss some of our on-going studies in this direction.

Biography: Jiawei Han is a Professor at the Department of Computer Science, University of Illinois at Urbana-Champaign. He has been working on research into data mining, data warehousing, database systems, data mining from spatiotemporal data, multimedia data, stream and RFID data, Web data, social network data, and biological data, with over 400 journal and conference publications.

He has chaired or served on over 100 program committees of international conferences and workshops, including PC co-chair for KDD, SDM, and ICDM conferences, and Americas Coordinator for a VLDB conference. He is also serving as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data.

He is a Fellow of ACM and IEEE, and has received 2004 ACM SIGKDD Innovations Award and 2005 IEEE Computer Society Technical Achievement Award. His book "Data Mining: Concepts and Techniques" (2nd ed., Morgan Kaufmann, 2006) has been popularly used as a textbook worldwide.

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