SELF-ORGANIZED CORONA GRAPHS: A DETERMINISTIC COMPLEX NETWORK MODEL WITH HIERARCHICAL STRUCTURE

Rohan Sharma (), Bibhas Adhikari and Tyll Krueger ()
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Rohan Sharma: Department of Computer Science Engineering, Bennett University, India
Bibhas Adhikari: Department of Mathematics, Indian Institute of Technology, Kharagpur, India
Tyll Krueger: Wroclaw University of Technology, Poland

Advances in Complex Systems (ACS), 2019, vol. 22, issue 06, 1-22

Abstract: In this paper, we propose a self-organization mechanism for newly appeared nodes during the formation of corona graphs that define a hierarchical pattern in the resulting corona graphs and we call it self-organized corona graphs (SoCG). We show that the degree distribution of SoCG follows power-law in its tail with power-law exponent approximately 2. We also show that the diameter is less equal to 4 for SoCG defined by any seed graph and for certain seed graphs, the diameter remains constant during its formation. We derive lower bounds of clustering coefficients of SoCG defined by certain seed graphs. Thus, the proposed SoCG can be considered as a growing network generative model which is defined by using the corona graphs and a self-organization process such that the resulting graphs are scale-free small-world highly clustered growing networks. The SoCG defined by a seed graph can also be considered as a network with a desired motif which is the seed graph itself.

Keywords: Corona graphs; self-organization; degree distribution; diameter; clustering coefficient; motifs (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S021952591950019X

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