Evolving Scale-Free Network Model with Tunable Clustering

Abstract

The Barabási-Albert (BA) model is extended to include the concept of local world and the microscopic event of adding edges. With probability $p$, we add a new node with $m$ edges which preferentially link to the nodes presented in the network; with probability $1-p$, we add $m$ edges among the present nodes. A node is preferentially selected by its degree to add an edge randomly among its neighbors. Using continuum theory and rate equation method we get the analytical expressions of the power-law degree distribution with exponent $\gamma =3$ and the clustering coefficient $c(k)\sim k^{-1}+c$. The analytical expressions are in good agreement with the numerical calculations.

8 pages, 4 figures, accepted by Int. J. Mod. Phys. B

References in corpus (2)

• The structure and function of complex networks
• Structural transitions in scale-free networks

Cited by in corpus (5)

• Bipartite Graphs as Models of Complex Networks
• Local-world evolving networks with tunable clustering
• Complex Network Properties of Chinese Natural Science Basic Research
• Effects of accelerating growth on the evolution of weighted complex networks
• Tuning degree distributions of scale-free networks