Challenges and Advances on Graph Mining

Speaker:        Professor Philip S. Yu
                Department of Computer Science
                University of Illinois at Chicago

Title:          "Challenges and Advances on Graph Mining"

Date:           Wednesday, 21 March 2012

Time:           3:00pm - 4:00pm

Venue:          Room 1504 (near lifts 25/26), opposite to LTG, HKUST

Abstract:

Mining graph data has become an important and active research topic in the
last decade, which has a wide variety of scientific and commercial
applications, such as in bioinformatics, security, the web, and social
networks. Previous research on graph classification mainly focuses on
mining significant subgraph features under single label settings for
supervised learning. The basic assumption is that a large number of
labeled graphs are available. However, labeling graph data is quite
expensive and time consuming for many real-world applications. In order to
reduce the labeling cost for graph data, in this talk we examine two
alternative approaches. The first approach uses semi-supervised feature
selection for graph classification to take advantage of the large amount
of unlabeled data, while the second approach exploits active learning to
judiciously select a small number of graph data to query for the label.
These problems are challenging and different from conventional
semi-supervised and active learning problems because there is no
predefined feature vector. The subgraph features need to be found
progressively during the mining process. Finally, we examine the issue of
multi-label classification to assign each graph data with a set of labels
simultaneously.The challenge is to estimate the dependence between the yet
to be determined subgraph features and the multiple labels of graphs.
Effective approaches to address these problems and overcome the challenges
will be discussed.

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Biography:

Philip S. Yu received the B.S. Degree in E.E. from National Taiwan
University, the M.S. and Ph.D. degrees in E.E. from Stanford University,
and the M.B.A. degree from New York University. He is currently a
Professor in the Department of Computer Science at the University of
Illinois at Chicago and also holds the Wexler Chair in Information
Technology.He spent most of his career at IBM Thomas J. Watson Research
Center and was manager of the Software Tools and Techniques group. His
research interests include data mining, privacy preserving data
publishing, data stream, Internet applications and technologies, and
database systems. Dr. Yu has published more than 650 papers in refereed
journals and conferences. He holds or has applied for more than 300 US
patents.

Dr. Yu is a Fellow of the ACM and the IEEE.He is the Editor-in-Chief of
ACM Transactions on Knowledge Discovery from Data.He is on the steering
committee of the IEEE Conference on Data Mining and ACM Conference on
Information and Knowledge Management and was a member of the IEEE Data
Engineering steering committee.He was the Editor-in-Chief of IEEE
Transactions on Knowledge and Data Engineering (2001-2004). He had also
served as an associate editor of ACM Transactions on the Internet
Technology and Knowledge and Information Systems.He had received several
IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding
Technical Achievement Award, 2 Research Division Awards and the 94th
plateau of Invention Achievement Awards.He was an IBM Master Inventor.Dr.
Yu received a Research Contributions Award from IEEE Intl. Conference on
Data Mining in 2003 and also an IEEE Region 1 Award for "promoting and
perpetuating numerous new electrical engineering concepts" in 1999.