Real-world graphs are seldom static. Applications that generate graph-structured data today do so continuously, giving rise to an underlying graph whose structure evolves over time. Mining these time-evolving graphs can be in- sightful, both from research and business perspectives. We present Tegra, a time-evolving graph processing system built on a general-purpose dataflow framework. We introduce Timelapse, a flexible abstraction that enables efficient analytics on evolving graphs by allowing graph-parallel stages to iterate over complete history of nodes. We use Timelapse to present two computational models, a temporal analysis model for performing compu- tations on multiple snapshots of an evolving graph, and a generalized incremental computation model for efficiently updating results of computations.