Abstract
Utility services provided by cloud computing rely on virtual customer communities forming spontaneously and evolving continuously. Clarifying the explicit boundaries of these communities is thus essential to the quality of utility services in cloud computing. Communities with overlapping features or prominent peripheral vertexes are usually typical irregular communities. Traditional community identification algorithms are limited in discovering irregular topological structures from CR networks, whereas these irregular shapes typically play an important role in finding prominent customers which are ignored in social CRM otherwise. We present a novel method of discovering irregular communities. It firstly finds and merges primitive maximal cliques and the irregular features of overlapping and prominent sparse vertices are further considered. An empirical case and a methodology comparison confirm the feasibility and efficiency of our approach.
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Liu, J., Zhou, J., Wang, J. et al. Irregular community discovery for cloud service improvement. J Supercomput 61, 317–336 (2012). https://doi.org/10.1007/s11227-010-0446-7
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DOI: https://doi.org/10.1007/s11227-010-0446-7