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Emergence of Scale-Free Leadership Structure in Social Recommender Systems

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  • Tao Zhou
  • Matúš Medo
  • Giulio Cimini
  • Zi-Ke Zhang
  • Yi-Cheng Zhang

Abstract

The study of the organization of social networks is important for the understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a “good get richer” mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems.

Suggested Citation

  • Tao Zhou & Matúš Medo & Giulio Cimini & Zi-Ke Zhang & Yi-Cheng Zhang, 2011. "Emergence of Scale-Free Leadership Structure in Social Recommender Systems," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-6, July.
    • Handle: RePEc:plo:pone00:0020648
    DOI: 10.1371/journal.pone.0020648
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    References listed on IDEAS

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    Cited by:

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    2. Zhang, Shouxu & Xie, Duosi & Yan, Weisheng, 2017. "Decentralized event-triggered consensus control strategy for leader–follower networked systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 498-508.
    3. Vidmer, Alexandre & Zeng, An & Medo, Matúš & Zhang, Yi-Cheng, 2015. "Prediction in complex systems: The case of the international trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 188-199.
    4. Mariko I Ito & Hisashi Ohtsuki & Akira Sasaki, 2018. "Emergence of opinion leaders in reference networks," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-21, March.
    5. Hao Liao & Rui Xiao & Giulio Cimini & Matúš Medo, 2014. "Network-Driven Reputation in Online Scientific Communities," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-18, December.
    6. Tai Huynh & Hien Nguyen & Ivan Zelinka & Dac Dinh & Xuan Hau Pham, 2020. "Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †," Sustainability, MDPI, vol. 12(7), pages 1-16, April.
    7. Zhang, N. & Huang, H. & Duarte, M. & Zhang, J., 2016. "Risk analysis for rumor propagation in metropolises based on improved 8-state ICSAR model and dynamic personal activity trajectories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 403-419.
    8. Yang, Xu-Hua & Chen, Guang & Chen, Sheng-Yong, 2013. "The impact of connection density on scale-free distribution in random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2547-2554.
    9. Cai Gao & Xin Lan & Xiaoge Zhang & Yong Deng, 2013. "A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-11, June.
    10. Colman, E.R. & Rodgers, G.J., 2014. "Local rewiring rules for evolving complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 80-89.
    11. Ramezani, Mohsen & Yaghmaee, Farzin, 2016. "A novel video recommendation system based on efficient retrieval of human actions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 607-623.

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