Coarse-graining the dynamics of network evolution: the rise and fall of a networked society

TL;DR

This paper presents a systematic coarse-graining method for analyzing the dynamics of evolving social networks, enabling faster simulations and system-level stability analysis through an equation-free approach.

Contribution

It introduces a novel coarse-graining framework for network evolution models, demonstrating its effectiveness on a social network case study.

Findings

Accelerated network simulations using coarse-grained models

Extraction of stability and bifurcation information from detailed models

Potential for broader system-level analysis tasks

Abstract

We explore a systematic approach to studying the dynamics of evolving networks at a coarse-grained, system level. We emphasize the importance of finding good observables (network properties) in terms of which coarse grained models can be developed. We illustrate our approach through a particular social network model: the "rise and fall" of a networked society [1]: we implement our low-dimensional description computationally using the equation-free approach and show how it can be used to (a) accelerate simulations and (b) extract system-level stability/bifurcation information from the detailed dynamic model. We discuss other system-level tasks that can be enabled through such a computer-assisted coarse graining approach.