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.