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Transparency Effect in the Emergence of Monopolies in Social Networks

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Abstract

A power law degree distribution is displayed in many complex networks. However, in most real social and economic networks, deviation from power-law behavior is observed. Such networks also have giant hubs far from the tail of the power law distribution. We propose a model based on information 'transparency' (i.e. how much information is visible to others), which can explain the power structure in societies with non-transparency in information delivery. The emergence of very high degree nodes is explained as a direct result of censorship. Based on these assumptions, we define four distinct transparency regions: perfectly non-transparent, low transparent, perfectly transparent regions and regions where information is exaggerated. We observe the emergence of some very high degree nodes in low transparency networks. We show that the low transparency networks are more vulnerable to attack and the controllability of low transparent networks is more difficult than for the others. Also, the low transparency networks have a smaller mean path length and higher clustering coefficients than the other regions.

Suggested Citation

  • Amir Hossein Shirazi & Ali Namaki & Amir Ahmad Roohi & Gholam Reza Jafari, 2013. "Transparency Effect in the Emergence of Monopolies in Social Networks," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(1), pages 1-1.
    • Handle: RePEc:jas:jasssj:2012-38-2
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    Cited by:

    1. Ausloos, Marcel & Saeedian, Meghdad & Jamali, Tayeb & Farahani, S. Vasheghani & Jafari, G. Reza, 2017. "How visas shape and make visible the geopolitical architecture of the planet," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 267-275.
    2. Sheida Hasani & Razieh Masoomi & Jamshid Ardalankia & Mohammadbashir Sedighi & Hamid Jafari, 2019. "Growth Dynamics of Value and Cost Trade-off in Temporal Networks," Papers 1908.11433, arXiv.org, revised Aug 2020.
    3. Rabbani, Fereshteh & Khraisha, Tamer & Abbasi, Fatemeh & Jafari, Gholam Reza, 2021. "Memory effects on link formation in temporal networks: A fractional calculus approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    4. Du, Haifeng & He, Xiaochen & Wang, Jingjing & Feldman, Marcus W., 2018. "Reversing structural balance in signed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 780-792.
    5. Hedayatifar, L. & Hassanibesheli, F. & Shirazi, A.H. & Vasheghani Farahani, S. & Jafari, G.R., 2017. "Pseudo paths towards minimum energy states in network dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 109-116.

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