title: Towards real-time community detection in large networks.
abstract: 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 1 x 10(6) nodes and about 58 x 10(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.
authors: University of Cambridge, Cambridge CB3 0FD, UK. ian.leung@cl.cam.ac.uk
authors: Ian X Y
authors: IX
authors: Leung
authors: Pan
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