Inducing Self-Organized Criticality in a network toy model by neighborhood assortativity

TL;DR

This paper introduces a simple network model with neighborhood assortativity that induces self-organized criticality, characterized by long-range correlations and anticorrelated node activity, combining numerical and analytical approaches.

Contribution

It proposes a novel neighborhood assortativity concept and demonstrates how it can induce SOC in a minimal network model, with analytical and numerical analysis.

Findings

SOC dynamics emerge with neighborhood assortativity

Long-range correlations and anticorrelation in activity are observed

Model statistics collapse into a single curve for different parameters

Abstract

Complex networks are a recent type of frameworks used to study complex systems with many interacting elements, such as Self-Organized Criticality (SOC). The network node's tendency to link to other nodes of similar type is characterized by assortative mixing. Real networks exhibit assortative mixing by vertex degree, however typical random network models, such as Erdos-Renyi or Barabasi-Albert, show no assortative arrangements. In this paper we introduce the neighborhood assortativity notion, as the tendency of a node to belong to a community (its neighborhood) showing an average property similar to its own. Imposing neighborhood assortative mixing by degree in a network toy model, SOC dynamics can be found. The long-range correlations resulting from the criticality have been characterized by means of fluctuation analysis and show an anticorrelation in the node's activity. The model contains only one parameter and its statistics plots for different values of the parameter can be collapsed into a single curve. The simplicity of the model allows performing numerical simulations and also to study analytically the statistics for a specific value of the parameter, making use of the Markov chains.