Computing on or over large graphs. Engines for calculating things on or about graphs. Which is everything, but, in particular, typically structures with of sparsity in some sense.
For specific applications of such computations, see e.g. graphical models or complex networks.
SNAP.py is engineered from the ground to do Stuff To Large Networks
It plugs into snapvx, a ‘nearly-convex’ solver for graph-like data.
Apache Giraph is a very large scale graph processor.
Apache Giraph is an iterative graph processing system built for high scalability. For example, it is currently used at Facebook to analyze the social graph formed by users and their connections. Giraph originated as the open-source counterpart to Pregel, the graph processing architecture developed at Google and described in a 2010 paper. Both systems are inspired by the Bulk Synchronous Parallel model of distributed computation introduced by Leslie Valiant. Giraph adds several features beyond the basic Pregel model, including master computation, sharded aggregators, edge-oriented input, out-of-core computation, and more. With a steady development cycle and a growing community of users worldwide, Giraph is a natural choice for unleashing the potential of structured datasets at a massive scale. To learn more, consult the User Docs section above.