Designer Nets from Local Strategies

TL;DR

This paper introduces a local strategy for constructing customizable scale-free networks with tunable properties, using a method based on redirection that requires only local information for network growth.

Contribution

It presents a novel local construction method for scale-free networks with adjustable degree distributions and clustering, improving upon global strategies.

Findings

The method can produce networks with desired degree distributions.

It allows tuning of clustering and other network characteristics.

Networks generated match theoretical scale-free properties.

Abstract

We propose a local strategy for constructing scale-free networks of arbitrary degree distributions, based on the redirection method of Krapivsky and Redner [Phys. Rev. E 63, 066123 (2001)]. Our method includes a set of external parameters that can be tuned at will to match detailed behavior at small degree k, in addition to the scale-free power-law tail signature at large k. The choice of parameters determines other network characteristics, such as the degree of clustering. The method is local in that addition of a new node requires knowledge of only the immediate environs of the (randomly selected) node to which it is attached. (Global strategies require information on finite fractions of the growing net.)