2013/11/25 by Vijay K. Samalam, Samalam, Vijay K, Vijay K Samalam
Physics and Astronomy · Mathematics · #Complex Network Analysis Techniques #Stochastic processes and statistical mechanics #Opinion Dynamics and Social Influence
paper · pdf · doi:10.48550/arxiv.1311.6401
Probabilistic networks display a wide range of high average clustering\ncoefficients independent of the number of nodes in the network. In particular,\nthe local clustering coefficient decreases with the degree of the subtending\nnode in a complicated manner not explained by any current models. While a\nnumber of hypotheses have been proposed to explain some of these observed\nproperties, there are no solvable models that explain them all. We propose a\nnovel growth model for both random and scale free networks that is capable of\npredicting both tunable clustering coefficients independent of the network\nsize, and the inverse relationship between the local clustering coefficient and\nnode degree observed in most networks.\n