EVOLVING SCALE-FREE NETWORK MODEL WITH TUNABLE CLUSTERING

2005/09/30 by Bing Wang, Huanwen Tang, Zhongzhi Zhang +1 · 1 citation
Computer Science · Physics and Astronomy · #Artificial intelligence #Cartography #Cellular Automata and Applications #Cluster analysis #Complex Network Analysis Techniques #Complex network #Computer science #Geography #Opinion Dynamics and Social Influence #Scale (ratio) #Scale-free network #World Wide Web #cond-mat.dis-nn

paper · pdf · doi:10.1142/s0217979205032437

published as Int. J. Mod. Phys. B 2005,19(26):3951-3959 · 8 pages, 4 figures, accepted by Int. J. Mod. Phys. B

openalex publication_date 2005/10/20 · arxiv created 2005/11/15 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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 the continuum theory and the rate equation method we get the analytical expressions of the power-law degree distribution with exponent γ=3 and the clustering coefficient c(k)~k -1 +c. The analytical expressions are in good agreement with the numerical calculations.

Citations

• Community structure in social and biological networks
• Structural transitions in scale-free networks
• Emergence of a Small World from Local Interactions: Modeling Acquaintance Networks
• Emergence of scaling in random networks
• Epidemic Threshold in Structured Scale-Free Networks
• Growing scale-free networks with small-world behavior
• Statistical mechanics of complex networks
• Evolution of networks
• The Structure and Function of Complex Networks

Cited by

• Tuning degree distributions: Departing from scale-free networks