GFT centrality: A new node importance measure for complex networks

Rahul Singh, Abhishek Chakraborty and B.S. Manoj

Physica A: Statistical Mechanics and its Applications, 2017, vol. 487, issue C, 185-195

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.

Keywords: Graph Fourier Transform; Node importance; GFT-C; Centrality measures; Graph signal processing; Complex networks (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:487:y:2017:i:c:p:185-195

DOI: 10.1016/j.physa.2017.06.018

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