  Many networks display community structure which identifies groups of nodes
within which connections are denser than between them. Detecting and
characterizing such community structure, which is known as community detection,
is one of the fundamental issues in the study of network systems. It has
received a considerable attention in the last years. Numerous techniques have
been developed for both efficient and effective community detection. Among
them, the most efficient algorithm is the label propagation algorithm whose
computational complexity is O(|E|). Although it is linear in the number of
edges, the running time is still too long for very large networks, creating the
need for parallel community detection. Also, computing community quality
metrics for community structure is computationally expensive both with and
without ground truth. However, to date we are not aware of any effort to
introduce parallelism for this problem. In this paper, we provide a parallel
toolkit to calculate the values of such metrics. We evaluate the parallel
algorithms on both distributed memory machine and shared memory machine. The
experimental results show that they yield a significant performance gain over
sequential execution in terms of total running time, speedup, and efficiency.
