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Computer Science Professors Ben Zhao and Heather Zheng have been awarded an NSF grant for their project “User Behavior Modeling and Prediction in Anonymous Social Networks”.

The project abstract states, “Human beings are diverse, and their online behavior is often unpredictable. In today’s data-driven world, providers of online services are collecting detailed and comprehensive server-side traces of user activity. These records or logs include detailed, time stamped logs of actions taken by users, often called clickstreams. Given their scale and level of detail, clickstreams present an enormous opportunity for research into user behavioral analysis and modeling. Understanding, modeling and predicting user behavior can dramatically improve the security of today’s online systems, while significantly advancing understanding of user behavior. This project develops a general platform for user behavioral modeling using clickstreams, with the goal of providing general tools for modeling user behavior in any application context. If successful, this approach will produce a generalized platform for identifying similar types of user behavior.  Easy work using a similar approach already produced significant results in the context of automatically detecting fake accounts and identities in online social networks.

The PIs will explore the use of clickstream similarity graphs, graphs designed to capture and model the similarity (or differences) between behavior logs of different users. By applying existing graph analysis techniques, these similarity graphs can identify general user behavioral patterns using semi-supervised learning techniques, and can be used to identify abnormal or unknown user behavior patterns. The researchers will use real detailed clickstreams from two online social networks (Renren and Whisper). The goal of the project is to make clickstream similarity graphs a general and practical user-modeling tool. The project will address three key challenges.  First, it will explore and address challenges of scale in users and trace length, so that the techniques can be applied to large user populations of hundreds of millions. Second, the project will quantify the level of dynamics in user behavior over time, develop techniques to incrementally modify or update user behavior models. Finally, the PIs will study issues in application specificity, how can we tune the tool for different dimensions of user behavior.”

The award includes funding in the amount of $499,929.

Ben Zhao is currently a Professor at the Computer Science department, U. C. Santa Barbara.  He completed his M.S. and Ph.D. degrees in Computer Science at U.C. Berkeley (2000, 2004), and his B.S. from Yale (1997). He is a recipient of the National Science Foundation's CAREER award, MIT Technology Review's TR-35 Award (Young Innovators Under 35), ComputerWorld Magazine's Top 40 Technology Innovators award, and the Google Faculty award. His work has been covered by media outlets such as New York Times, Boston Globe, MIT Tech Review, and Slashdot.  He has published over 100 publications in areas of security and privacy, networked and distributed systems, wireless networks and data-intensive computing. Finally, he has served as program chair for top conferences (WOSN, WWW 2013 OSN track, IPTPS, IEEE P2P), and is a co-founder and on the steering committee of the new ACM Conference on Online Social Networks (COSN).

To learn more about Professor Zhao and his work, visit his website here.

 


Heather Zheng is a Professor at the Department of Computer Science, University of California, Santa Barbara. She joined UCSB in 2005 after spending six years in industry research labs (Bell-Labs, NJ and Microsoft Research Asia). Some of her recent awards include the MIT Technology Review’s Young Innovator (TR-35) award (2005), and the fellow of the World Technology Network (2006).  She was the TPC co-Chair of the IEEE DySPAN conference in 2011, and workshop co-chair of SDR’2009.

To learn more about Professor Zheng and her work, visit her website here.