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Group detection in complex networks: An algorithm and comparison of the state of the art

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  • Šubelj, Lovro
  • Bajec, Marko

Abstract

Complex real-world networks commonly reveal characteristic groups of nodes like communities and modules. These are of value in various applications, especially in the case of large social and information networks. However, while numerous community detection techniques have been presented in the literature, approaches for other groups of nodes are relatively rare and often limited in some way. We present a simple propagation-based algorithm for general group detection that requires no a priori knowledge and has near ideal complexity. The main novelty here is that different types of groups are revealed through an adequate hierarchical group refinement procedure. The proposed algorithm is validated on various synthetic and real-world networks, and rigorously compared against twelve other state-of-the-art approaches on group detection, hierarchy discovery and link prediction tasks. The algorithm is comparable to the state of the art in community detection, while superior in general group detection and link prediction. Based on the comparison, we also discuss some prominent directions for future work on group detection in complex networks.

Suggested Citation

  • Šubelj, Lovro & Bajec, Marko, 2014. "Group detection in complex networks: An algorithm and comparison of the state of the art," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 397(C), pages 144-156.
    • Handle: RePEc:eee:phsmap:v:397:y:2014:i:c:p:144-156
    DOI: 10.1016/j.physa.2013.12.003
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    References listed on IDEAS

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    7. Šubelj, Lovro & Bajec, Marko, 2011. "Community structure of complex software systems: Analysis and applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2968-2975.
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    Citations

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    Cited by:

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    4. Filiposka, Sonja & Juiz, Carlos, 2015. "Community-based complex cloud data center," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 356-372.

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