Self-Coordinated Corona Graphs: a model for complex networks
arXiv:1509.08773
Abstract
Recently, real world networks having constant/shrinking diameter along with power-law degree distribution are observed and investigated in literature. Taking an inspiration from these findings, we propose a deterministic complex network model, which we call Self-Coordinated Corona Graphs (SCCG), based on the corona product of graphs. As it has also been established that self coordination/organization of nodes gives rise to emergence of power law in degree distributions of several real networks, the networks in the proposed model are generated by the virtue of self coordination of nodes in corona graphs. Alike real networks, the SCCG inherit motifs which act as the seed graphs for the generation of SCCG. We also analytically prove that the power law exponent of SCCG is approximately and the diameter of SCCG produced by a class of motifs is constant. Finally, we compare different properties of the proposed model with that of the BA and Pseudofractal scale-free models for complex networks.
arXiv comment21 pages, 31 figures
References in corpus (15)
- Emergence of scaling in random networks
- Statistical mechanics of complex networks
- The structure and function of complex networks
- Assortative mixing in networks
- Hierarchical organization of modularity in metabolic networks
- Evolution of networks
- Hierarchical Organization in Complex Networks
- Biological network comparison using graphlet degree distribution
- Kronecker Graphs: An Approach to Modeling Networks
- Pseudofractal Scale-free Web
- Evolution of reference networks with aging
- A Geometric Fractal Growth Model for Scale Free Networks
- Duplication-divergence model of protein interaction network
- Universal fractal scaling of self-organized networks
- Degree Relations of Triangles in Real-world Networks and Models