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Modeling online social signed networks

Author

Listed:
  • Li, Le
  • Gu, Ke
  • Zeng, An
  • Fan, Ying
  • Di, Zengru

Abstract

People’s online rating behavior can be modeled by user–object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user–object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user’s signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

Suggested Citation

  • Li, Le & Gu, Ke & Zeng, An & Fan, Ying & Di, Zengru, 2018. "Modeling online social signed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 345-352.
    • Handle: RePEc:eee:phsmap:v:495:y:2018:i:c:p:345-352
    DOI: 10.1016/j.physa.2017.12.089
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    Citations

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    Cited by:

    1. Shanahan, Tyler & Tran, Trang P. & Taylor, Erik C., 2019. "Getting to know you: Social media personalization as a means of enhancing brand loyalty and perceived quality," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 57-65.
    2. Gu, Ke & Fan, Ying & Di, Zengru, 2020. "How to predict recommendation lists that users do not like," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    3. Gu, Ke & Fan, Ying & Zeng, An & Zhou, Jianlin & Di, Zengru, 2018. "Analysis on large-scale rating systems based on the signed network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 99-109.

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