  Graphs may be used to represent many different problem domains -- a concrete
example is that of detecting communities in social networks, which are
represented as graphs. With big data and more sophisticated applications
becoming widespread in recent years, graph processing has seen an emergence of
requirements pertaining data volume and volatility. This multidisciplinary
study presents a review of relevant distributed graph processing systems.
Herein they are presented in groups defined by common traits (distributed
processing paradigm, type of graph operations, among others), with an overview
of each system's strengths and weaknesses. The set of systems is then narrowed
down to a set of two, upon which quantitative analysis was performed. For this
quantitative comparison of systems, focus was cast on evaluating the
performance of algorithms for the problem of detecting communities. To help
further understand the evaluations performed, a background is provided on graph
clustering.
