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Towards Real-Time Community Detection in Large Networks
Citation key LHLC-TRTCDLN-09
Author Leung, Ian X.Y. and Hui, Pan and Lio', Pietro and Crowcroft, Jon
Pages 1–10
Year 2009
ISSN 1539-3755
Online ISSN 1550-2376
DOI http://dx.doi.org/10.1103/PhysRevE.79.066107
Journal Physical Review E (Statistical, Nonlinear, and Soft Matter Physics)
Volume 79
Number 6
Month June
Note Article ID: 066107
Abstract 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.
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