Big Graph Data Science

For our July Meetup, we are thrilled to have Prof. Lise Getoor, until recently at the University of Maryland, College Park, talking about cutting-edge algorithms for working with very large data sets representing connections among entities, such as social network data. The techniques required to make sense of this sort of graphical data are highly in demand, across a wide range of domains. Prof. Getoor's talk will survey the state of the art in graph representation and analytics.

NOTE: The venue this month is the Microsoft offices in Chevy Chase, MD. The building is right at the North entrance to the Friendship Heights Metro stop, and there are a number of good options for parking.

Agenda:

• 6:30pm -- Networking and Refreshments

• 7:00pm -- Introduction

• 7:15pm -- Presentation and discussion

• 8:30pm -- Adjourn for Data Drinks (Clyde's, 5441 Wisconsin Ave.)

Abstract:

One of the challenges in big data analytics lies in being able to reason collectively about extremely large, heterogeneous, incomplete, noisy interlinked data.  We need data science techniques which can represent and reason effectively with this form of rich and multi-relational graph data.  In this presentation, I will describe some common collective inference patterns needed for graph data including: collective classification (predicting missing labels for nodes in a
network), link prediction (predicting potential edges), and entity resolution (determining when two nodes refer to the same underlying entity).  I will describe two key capabilities required, relational feature construction and collective inference, and briefly describe some of the cutting edge analytic tools being developed within the machine learning, AI, and database communities.

Bio:

Lise Getoor is Professor in the Computer Science Department at the University of California, Santa Cruz and helps lead their Data Science Initiative.  Prior to that, she was a Professor in the Computer Science Department, University of Maryland, College Park (2001 to 2013). Her research areas include machine learning and reasoning under uncertainty; in addition she works in data management, visual analytics and social network analysis. She is a recipient of an NSF Career Award and eight best paper and best student paper awards. She received her PhD from Stanford University, her Master’s degree from University of California, Berkeley, and her undergraduate degree from the University of California, Santa Barbara.

Sponsors:

This event is sponsored by MicrosoftClouderaStatistics.comLivingSocialIBM Analytics Solution CenterNovetta SolutionsElder Research, and Five 9 Group.

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