  Graph processing is an increasingly important domain of computer science,
with applications in data and network analysis, among others. Target graphs in
these applications are often large, leading to the creation of "big data"
systems designed to provide the scalability needed to analyze these graphs
using parallel processing. However, researchers have shown that while these
systems often provide scalability, they also often introduce overheads that
exceed the benefits they provide, sometimes resulting in lower absolute
performance than even simple serial implementations. This report studies the
viability and performance of the actor model to implement scalable concurrent
programs to perform common graph computations. We show that relatively simple
actor-based implementations outperform both dedicated graph processing systems
and the benchmark serial implementations.
