Towards real-time community detection in large networks

The recent boom of large-scale online social networks (OSNs) both enables and necessitates the use of parallelizable 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 1x10(6) nodes and about 58x10(6) directed edges. Experiments and benchmarks reveal that the extended algorithm is not only faster but its community detection accuracy compares favorably over popular modularity-gain optimization algorithms known to suffer from their resolution limits.