Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5102559 | Physica A: Statistical Mechanics and its Applications | 2017 | 23 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Mathematical Physics
Authors
Rahul Singh, Abhishek Chakraborty, B.S. Manoj,