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Sunday, August 11 • 10:30am - 11:30am
SNAKDD: Keynote Speech 2: Challenges and Advances on Social Network Mining : Philip S. Yu

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ABSTRACT: Mining social network data has become an important and active research topic in the last decade, which has a wide variety of scientific and commercial applications. We first consider the survivability issue of communities. Among communities, we notice that some of them are magnetic to people. A magnet community is such a community that attracts significantly more people's interests and attentions than other communities of similar topics. We will study the magnet community identification problem. Next we consider the cascading effect of nodes in a network. This is sometime referred to as the "too big to fail" problem in the financial world, describing certain financial institutions which are so large and so interconnected that their failure will be disastrous to the economy, and which therefore must be supported by government when they face difficulty. We call such high impact entities shakers. To discover shakers, we introduce the concept of a cascading graph to capture the causality relationships among evolving entities over some period of time, and then infer shakers from the graph. In a cascading graph, nodes represent entities and weighted links represent the causality effects. Finally, we consider how to capture anomaly behavior in a network. Specifically, we look into the spam review detection problem. Online reviews provide valuable information about products and services to consumers. However, spammers are joining the community trying to mislead readers by writing fake reviews. We propose a novel concept of a heterogeneous review graph to capture the relationships among reviewers, reviews and stores that the reviewers have reviewed. We explore how interactions between nodes in this graph can reveal the cause of spam and propose an iterative model to identify suspicious reviewers.

BIO: Philip S. Yu is currently a Distinguished 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 740 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. Dr. Yu received an IEEE Computer Society 2013 Technical Achievement Award for "pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data" and a Research Contributions


Philip S. Yu

UIC Distinguished Professor and Wexler Chair in Information Technology, Department of Computer Science, University of Illinois at Chicago

Sunday August 11, 2013 10:30am - 11:30am CDT
Chicago 8