  We perform a detailed analysis of the C++ implementation of the Cluster
Affiliation Model for Big Networks (BigClam) on the Stanford Network Analysis
Project (SNAP). BigClam is a popular graph mining algorithm that is capable of
finding overlapping communities in networks containing millions of nodes. Our
analysis shows a key stage of the algorithm - determining if a node belongs to
a community - dominates the runtime of the implementation, yet the computation
is not parallelized. We show that by parallelizing computations across multiple
threads using OpenMP we can speed up the algorithm by 5.3 times when solving
large networks for communities, while preserving the integrity of the program
and the result.
