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GFT centrality: A new node importance measure for complex networks

Author

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  • Singh, Rahul
  • Chakraborty, Abhishek
  • Manoj, B.S.

Abstract

Identifying central nodes is very crucial to design efficient communication networks or to recognize key individuals of a social network. In this paper, we introduce Graph Fourier Transform Centrality (GFT-C), a metric that incorporates local as well as global characteristics of a node, to quantify the importance of a node in a complex network. GFT-C of a reference node in a network is estimated from the GFT coefficients derived from the importance signal of the reference node. Our study reveals the superiority of GFT-C over traditional centralities such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and Google PageRank centrality, in the context of various arbitrary and real-world networks with different degree–degree correlations.

Suggested Citation

  • Singh, Rahul & Chakraborty, Abhishek & Manoj, B.S., 2017. "GFT centrality: A new node importance measure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 487(C), pages 185-195.
    • Handle: RePEc:eee:phsmap:v:487:y:2017:i:c:p:185-195
    DOI: 10.1016/j.physa.2017.06.018
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    Cited by:

    1. Col, Alcebiades Dal & Petronetto, Fabiano, 2023. "Graph regularization centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).

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