  The recent boom of large-scale Online Social Networks (OSNs) both enables and
necessitates the use of parallelisable and scalable computational techniques
for their analysis. We examine the problem of real-time community detection and
a recently proposed linear time - O(m) on a network with m edges - label
propagation or "epidemic" community detection algorithm. We identify
characteristics and drawbacks of the algorithm and extend it by incorporating
different heuristics to facilitate reliable and multifunctional real-time
community detection. With limited computational resources, we employ the
algorithm on OSN data with 1 million nodes and about 58 million directed edges.
Experiments and benchmarks reveal that the extended algorithm is not only
faster but its community detection accuracy is compared favourably over popular
modularity-gain optimization algorithms known to suffer from their resolution
limits.
