Abstract
The increases in volume and inter-connectivity of graph data
result in the emergence of recent scalable and high perfor-
mance graph analysis systems. Those systems provide dif-
ferent graph representation models, a variety of querying
interfaces and libraries, and several underlying computation
models. As a consequence, such diversities complicate in-
situ choices of best platforms for data scientists according
to their desired graph analysis tasks. In this poster pre-
sentation, we compare recent high performance and scalable
graph analysis systems in distributed and supercomputer-
based processing environments with two important graph
analysis workloads: graph mining and graph pattern match-
ing. We also compare those systems in terms of expressive-
ness and suitability of their querying interfaces for the two
distinct workloads.
