2005/10/17 by Kazuhiro Takemoto, Chikoo Oosawa
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Opinion Dynamics and Social Influence #cond-mat.dis-nn #cond-mat.stat-mech #physics.soc-ph #q-bio.MN
paper · pdf · doi:10.1103/physreve.72.046116
published as Phys. Rev. E 72, 046116 (2005) · 8 pages, 8 figures
openalex publication_date 2005/10/17 · arxiv created 2005/11/22 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a model for evolving networks by merging building blocks represented as complete graphs, reminiscent of modules in biological system or communities in sociology. The model shows power-law degree distributions, power-law clustering spectra, and high average clustering coefficients independent of network size. The analytical solutions indicate that a degree exponent is determined by the ratio of the number of merging nodes to that of all nodes in the blocks, demonstrating that the exponent is tunable, and are also applicable when the blocks are classical networks such as Erdös-Rényi or regular graphs. Our model becomes the same model as the Barabási-Albert model under a specific condition.