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Sampling promotes community structure in social and information networks

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  • Blagus, Neli
  • Šubelj, Lovro
  • Weiss, Gregor
  • Bajec, Marko

Abstract

Any network studied in the literature is inevitably just a sampled representative of its real-world analogue. Additionally, network sampling is lately often applied to large networks to allow for their faster and more efficient analysis. Nevertheless, the changes in network structure introduced by sampling are still far from understood. In this paper, we study the presence of characteristic groups of nodes in sampled social and information networks. We consider different network sampling techniques including random node and link selection, network exploration and expansion. We first observe that the structure of social networks reveals densely linked groups like communities, while the structure of information networks is better described by modules of structurally equivalent nodes. However, despite these notable differences, the structure of sampled networks exhibits stronger characterization by community-like groups than the original networks, irrespective of their type and consistently across various sampling techniques. Hence, rich community structure commonly observed in social and information networks is to some extent merely an artifact of sampling.

Suggested Citation

  • Blagus, Neli & Šubelj, Lovro & Weiss, Gregor & Bajec, Marko, 2015. "Sampling promotes community structure in social and information networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 206-215.
    • Handle: RePEc:eee:phsmap:v:432:y:2015:i:c:p:206-215
    DOI: 10.1016/j.physa.2015.03.048
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    References listed on IDEAS

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    1. Borut Lužar & Zoran Levnajić & Janez Povh & Matjaž Perc, 2014. "Community Structure and the Evolution of Interdisciplinarity in Slovenia's Scientific Collaboration Network," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-5, April.
    2. L. Šubelj & M. Bajec, 2012. "Ubiquitousness of link-density and link-pattern communities in real-world networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 85(1), pages 1-11, January.
    3. Lovro Šubelj & Slavko Žitnik & Neli Blagus & Marko Bajec, 2014. "Node Mixing And Group Structure Of Complex Software Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 17(07n08), pages 1-26.
    4. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    5. Blagus, Neli & Šubelj, Lovro & Bajec, Marko, 2014. "Assessing the effectiveness of real-world network simplification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 134-146.
    6. J. Reichardt & D. R. White, 2007. "Role models for complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 60(2), pages 217-224, November.
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    1. Blagus, Neli & Šubelj, Lovro & Bajec, Marko, 2017. "Empirical comparison of network sampling: How to choose the most appropriate method?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 477(C), pages 136-148.

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