Scaling and percolation in the small-world network model

TL;DR

This paper analyzes the small-world network model by Watts and Strogatz, identifying a critical length-scale that governs network behavior, deriving scaling laws, and applying the model to disease spread through percolation theory.

Contribution

It introduces a non-trivial length-scale in the small-world model, derives critical exponents and scaling functions, and applies percolation theory to understand epidemic thresholds.

Findings

Identified a critical length-scale diverging as randomness tends to zero.

Derived finite size scaling form for average vertex-vertex distance.

Estimated percolation threshold for epidemic outbreak in small-world networks.

Abstract

In this paper we study the small-world network model of Watts and Strogatz, which mimics some aspects of the structure of networks of social interactions. We argue that there is one non-trivial length-scale in the model, analogous to the correlation length in other systems, which is well-defined in the limit of infinite system size and which diverges continuously as the randomness in the network tends to zero, giving a normal critical point in this limit. This length-scale governs the cross-over from large- to small-world behavior in the model, as well as the number of vertices in a neighborhood of given radius on the network. We derive the value of the single critical exponent controlling behavior in the critical region and the finite size scaling form for the average vertex-vertex distance on the network, and, using series expansion and Pade approximants, find an approximate analytic form for the scaling function. We calculate the effective dimension of small-world graphs and show that this dimension varies as a function of the length-scale on which it is measured, in a manner reminiscent of multifractals. We also study the problem of site percolation on small-world networks as a simple model of disease propagation, and derive an approximate expression for the percolation probability at which a giant component of connected vertices first forms (in epidemiological terms, the point at which an epidemic occurs). The typical cluster radius satisfies the expected finite size scaling form with a cluster size exponent close to that for a random graph. All our analytic results are confirmed by extensive numerical simulations of the model.