Vijay K Samalam | Searcharxiv

arXiv2013

A model for generating tunable clustering coefficients independent of the number of nodes in scale free and random networks

Probabilistic networks display a wide range of high average clustering coefficients independent of the number of nodes in the network. In particular, the local clustering coefficient decreases with the degree of the subtending node in a complicated manner not explained by any current models. While a number of hypotheses have been proposed to explain some of these observed properties, there are no solvable models that explain them all. We propose a novel growth model for both random and scale free networks that is capable of predicting both tunable clustering coefficients independent of the network size, and the inverse relationship between the local clustering coefficient and node degree observed in most networks.

physics.soc-ph↗

arXiv2012

Preferential attachment alone is not sufficient to generate scale free random networks

Many networks exhibit scale free behavior where their degree distribution obeys a power law for large vertex degrees. Models constructed to explain this phenomena have relied on preferential attachment where the networks grow by the addition of both vertices and edges, and the edges attach themselves to a vertex with a probability proportional to its degree. Simulations hint, though not conclusively, that both growth and preferential attachment are necessary for scale free behavior. We derive analytic expressions for degree distributions for networks that grow by the addition of edges to a fixed number of vertices, based on both linear and non-linear preferential attachment, and show that they fall off exponentially as would be expected for purely random networks. From this we conclude that preferential attachment alone might be necessary but is certainly not a sufficient condition for generating scale free networks.

physics.soc-ph↗