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GraphLab: Large-Scale Machine Learning on Graphs

Carlos Guestrin (Apple | University of Washington ), Joseph Gonzalez (UC Berkeley)
Hadoop & Beyond Murray Hill Suite
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From social networks, to protein molecules and the web, graphs encode structure and context, enable advanced machine learning, and are rapidly becoming the future of big-data. In this talk we will present the next generation of GraphLab, an open-source platform and machine learning framework designed to process graphs with hundreds of billions of vertices and edges on hardware ranging from a single mac-mini to the cloud.

We will present the GraphLab programming abstraction that blends a vertex and edge centric view of computation to enable users to express algorithms that can be efficiently executed on hardware ranging from multi-core to the cloud. We will describe some of the technical innovations that form the foundation of the GraphLab runtime and enable unprecedented scaling performance. Using PageRank as a running example we will show how to design, implement, and execute graph analytics on real-world twitter-scale graphs. Finally, we will present the GraphLab machine learning frameworks and demonstrate how they can be used to identify communities and important individuals, target customers, and extract meaning from text data.

Photo of Carlos Guestrin

Carlos Guestrin

Apple | University of Washington

Carlos is the CEO of GraphLab, and the Amazon Professor of Machine Learning in Computer Science & Engineering at the University of Washington. A world-recognized leader in the field of Machine Learning, Carlos was named one of the 2008 “Brilliant 10″ by Popular Science Magazine, received the 2009 IJCAI Computers and Thought Award for his contributions to Artificial Intelligence, and a Presidential Early Career Award for Scientists and Engineers (PECASE).

Photo of Joseph Gonzalez

Joseph Gonzalez

UC Berkeley

I am a postdoc in the AMPLab at UC Berkeley where I am continuing work on large-scale systems for machine learning as well as the GraphLab project. As a graduate student I worked with Carlos Guestrin in the Machine Learning Department at Carnegie Mellon University (CMU). My research addresses the challenges of designing and building large-scale machine learning algorithms and systems. In particular, my thesis focuses on large-scale structured machine learning using probabilistic graphical models that are capable of reasoning about billions of related random variables. The resulting algorithms and systems have achieved state-of-the-art performance in tasks ranging from predicting ad preferences in social networks to solving complex protein modeling tasks. As part of my thesis work we created GraphLab , a framework that dramatically simplifies the design and implementation of high-performance large-scale machine learning systems.

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Comments

Marek K Kolodziej
10/30/2013 4:31pm EDT

Would it be possible to post the slides here, like the other speakers have?

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Ken Krugler
10/25/2013 2:15pm EDT

Hi there – I’m hoping you could include an architectural comparison with other graph solutions such as Titan, thanks!

Kathy Yu
10/24/2013 11:45am EDT

Hi Heather – all sessions, tutorials, and keynotes will be recorded as a part of the Complete Video Collection, available about three weeks after the conference. You can sign up here to get notified when it becomes available. Hope that helps!

Heather Umberger
10/24/2013 7:52am EDT

Any chance this will be available virtually? Either live or published?

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