  Community detection is an essential task in network analysis as it helps
identify groups and patterns within a network. High-speed community detection
algorithms are necessary to analyze large-scale networks in a reasonable amount
of time. Researchers have made significant contributions in the development of
high-speed community detection algorithms, particularly in the area of
label-propagation based disjoint community detection. These algorithms have
been proven to be highly effective in analyzing large-scale networks in a
reasonable amount of time. However, it is important to evaluate the performance
and accuracy of these existing methods to determine which algorithm is best
suited for a particular type of network and specific research problem. In this
report, we investigate the RAK, COPRA, and SLPA, three label-propagation-based
static community discovery techniques. We pay close attention to each
algorithm's minute details as we implement both its single-threaded and
multi-threaded OpenMP-based variants, making any necessary adjustments or
optimizations and obtaining the right parameter values. The RAK algorithm is
found to perform well with a tolerance of 0.05 and OpenMP-based strict RAK with
12 threads was 6.75x faster than the sequential non-strict RAK. The COPRA
algorithm works well with a single label for road networks and max labels of
4-16 for other classes of graphs. The SLPA algorithm performs well with
increasing memory size, but overall doesn't offer a favourable return on
investment. The RAK algorithm is recommended for label-propagation based
disjoint community detection.
