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Robustness of centrality measures against network manipulation

Author

Listed:
  • Niu, Qikai
  • Zeng, An
  • Fan, Ying
  • Di, Zengru

Abstract

Node centrality is an important quantity to consider in studying complex networks as it is related to many applications ranging from the prediction of network structure to the control of dynamics on networks. In the literature, much effort has been devoted to design new centrality measurements. However, the reliability of these centrality measurements has not been fully assessed, particularly with respect to the fact that many real networks are facing different kinds of manipulations such as addition, removal or rewiring of links. In this paper, we focus on the robustness of classic centrality measures against network manipulation. Our analysis is based on both artificial and real networks. We find that the centrality measurements are generally more robust in heterogeneous networks. Biased link manipulation could more seriously distort the centrality measures than random link manipulation. Moreover, the top part of the centrality ranking is more resistant to manipulation.

Suggested Citation

  • Niu, Qikai & Zeng, An & Fan, Ying & Di, Zengru, 2015. "Robustness of centrality measures against network manipulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 124-131.
    • Handle: RePEc:eee:phsmap:v:438:y:2015:i:c:p:124-131
    DOI: 10.1016/j.physa.2015.06.031
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    Cited by:

    1. Zarghami, Seyed Ashkan & Dumrak, Jantanee, 2021. "Unearthing vulnerability of supply provision in logistics networks to the black swan events: Applications of entropy theory and network analysis," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Tsugawa, Sho & Kimura, Kazuma, 2018. "Identifying influencers from sampled social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 294-303.

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