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Directed networks’ different link formation mechanisms causing degree distribution distinction

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  • Stefan Behfar

    (BETA - Bureau d'Économie Théorique et Appliquée - INRA - Institut National de la Recherche Agronomique - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique)

  • Ekaterina Turkina

    (HEC Montréal - HEC Montréal)

  • Patrick Cohendet

    (HEC Montréal - HEC Montréal)

  • Thierry Burger-Helmchen

    (BETA - Bureau d'Économie Théorique et Appliquée - INRA - Institut National de la Recherche Agronomique - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique)

Abstract

Within undirected networks, scientists have shown much interest in presenting power-law features within complex networks. For instance, Barabási and Albert (1999) claimed that a common property of many large networks was that vertex connectivity follows scale-free power-law distribution, and in another study Barabási et al. (2002) showed power law evolution in the social network of scientific collaboration. At the same time, Jiang et al. (2011) discussed deviation from power-law distribution ; others indicated that size effect (Bagrow et al., 2008) ,information filtering mechanism (Mossa et al., 2002), and birth and death process (Shi et al., 2005) could account for this deviation. Within directed networks, many authors have considered that outlinks follow a similar mechanism of creation as inlinks' formation (Faloutsos et al., 1999 ; Krapivsky et al., 2001 ; Tanimoto, 2009) with link creation rate being the linear function of node degree, and a resulting power-law shape for both indegree and outdegree distribution. Some other authors have made an assumption that directed networks, such as scientific collaboration or citation, behave as undirected, resulting in a power-law degree distribution accordingly (Barabási et al., 2002). At the same time, we claim. Outlinks feature different degree distributions from inlinks ; where different link formation mechanisms cause the distribution distinctions, in/out degree distribution distinction holds for different levels of system decomposition ; therefore this distribution distinction is a property of directed networks. First, we emphasize in/out link formation mechanisms as causal factors for distinction between indegree and outdegree distributions (where this distinction has already been noticed in Barker et al. (2010) and Baxter et al. (2006)) within a sample network of OSS projects as well as Java software corpus as a network.Second, we analyze whether this distribution distinction holds for different levels of system decomposition : open-source-software (OSS) project–project dependency within a cluster, package–package dependency within a project and class–class dependency within a package. We conclude that indegree and outdegree dependencies do not lead to similar type of degree distributions, implying that indegree dependencies follow overall power-law distribution (or power-law with flat-top or exponential cut-off in some cases), while outdegree dependencies do not follow heavy-tailed distribution.

Suggested Citation

  • Stefan Behfar & Ekaterina Turkina & Patrick Cohendet & Thierry Burger-Helmchen, 2016. "Directed networks’ different link formation mechanisms causing degree distribution distinction," Post-Print hal-02189758, HAL.
  • Handle: RePEc:hal:journl:hal-02189758
    DOI: 10.1016/j.physa.2016.06.035
    Note: View the original document on HAL open archive server: https://hal.science/hal-02189758
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    References listed on IDEAS

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    1. J. Jiang & R. Wang & Q. A. Wang, 2011. "Network model of deviation from power-law distribution in complex network," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 79(1), pages 29-33, January.
    2. Ergün, G. & Rodgers, G.J., 2002. "Growing random networks with fitness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 303(1), pages 261-272.
    3. James Ma & Daniel Zeng & Huimin Zhao, 2012. "Modeling the growth of complex software function dependency networks," Information Systems Frontiers, Springer, vol. 14(2), pages 301-315, April.
    4. Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2010. "Complex stock trading network among investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4929-4941.
    5. Xin-Jian Xu & Liu-Ming Zhang & Li-Jie Zhang, 2010. "Mutual Selection In Network Evolution: The Role Of The Intrinsic Fitness," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(01), pages 129-135.
    6. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
    7. James P. Bagrow & Jie Sun & Daniel ben-Avraham, 2007. "Phase transition in the rich-get-richer mechanism due to finite-size effects," Papers 0712.2220, arXiv.org, revised May 2008.
    8. Matthew O. Jackson & Brian W. Rogers, 2007. "Meeting Strangers and Friends of Friends: How Random Are Social Networks?," American Economic Review, American Economic Association, vol. 97(3), pages 890-915, June.
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    Cited by:

    1. Bütün, Ertan & Kaya, Mehmet, 2019. "A pattern based supervised link prediction in directed complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1136-1145.

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