Graph (network) data such as social networks are, like other modern big data, sheer in volume, come from a variety of sources, and evolve at a high velocity. To deal with such data, traditionally fast algorithms, i.e. those that can quickly process static graph data on a single machine, are no longer considered sufficient. Modern algorithms need to work across different computational paradigms; for example, they must be distributed, so that they can handle graph data stored at multiple machines, and dynamic, so that they can handle rapid changes. The overall goal of this project is to develop a new generation of algorithms that work across different computation paradigms and at the same time use insights achieved through lenses provided by modern computation models to make progress on long-standing questions about graph algorithms in the field. Better algorithms would allow computing devices to do the same tasks with fewer resources such as time, energy, or hardware.