There has been significant interest in high-performance graph processing due to their applications in many domains, including social network and Web analytics, machine learning, biology, and physical simulations. However, writing efficient parallel graph programs for processing the large-scale graphs available today can be very difficult and time consuming, and therefore it is important to have tools that make the task easier. This talk will cover our recent work on high-level frameworks for parallel graph processing, for both static graphs and streaming graphs.