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  "note": "Analyzing large graphs provides valuable insights for social networking and web companies in content ranking and rec- ommendations. While numerous graph processing systems have been developed and evaluated on available benchmark graphs of up to 6.6B edges, they often face significant dif- ficulties in scaling to much larger graphs. Industry graphs can be two orders of magnitude larger - hundreds of bil- lions or up to one trillion edges. In addition to scalability challenges, real world applications often require much more complex graph processing workflows than previously evalu- ated. In this paper, we describe the usability, performance, and scalability improvements we made to Apache Giraph, an open-source graph processing system, in order to use it on Facebook-scale graphs of up to one trillion edges. We also describe several key extensions to the original Pregel model that make it possible to develop a broader range of production graph applications and workflows as well as im- prove code reuse. Finally, we report on real-world operations as well as performance characteristics of several large-scale production applications.",
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