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November 6, 2015

Big Graph Data Science | Lise Getoor | WiDS 2015

About This Video

One of the challenges in big data analytics lies in being able to reason collectively about extremely large, heterogeneous, incomplete, and noisy interlinked data. We need data science techniques that can represent and reason effectively with this form of rich and multi-relational graph data. In this talk, I will describe some common inference patterns needed for graph data including: collective classification (predicting missing labels for nodes), link prediction (predicting potential edges), and entity resolution (determining when two nodes refer to the same underlying entity). I will describe some key capabilities required to solve these problems, and finally I will describe a highly scalable open-source probabilistic programming language being developed within my group to solve these challenges.

In This Video
Professor, University of Maryland Institute for Advanced Computer Studies