Automatic Generation of Adaptive Network Models based on Similarity to the Desired Complex Network

TL;DR

This paper introduces NetMix, an adaptive network modeling framework using genetic algorithms to generate graphs that closely match specific desired properties of complex networks.

Contribution

It presents a novel automatic approach for evolving network models tailored to target network features, improving over traditional static models.

Findings

NetMix outperforms baseline models in matching target network features

Genetic algorithms effectively evolve network models to fit desired properties

The framework adapts to various complex network characteristics

Abstract

Complex networks have become powerful mechanisms for studying a variety of realworld systems. Consequently, many human-designed network models are proposed that reproduce nontrivial properties of complex networks, such as long-tail degree distribution or high clustering coefficient. Therefore, we may utilize network models in order to generate graphs similar to desired networks. However, a desired network structure may deviate from emerging structure of any generative model, because no selected single model may support all the needed properties of the target graph and instead, each network model reflects a subset of the required features. In contrast to the classical approach of network modeling, an appropriate modern network model should adapt the desired features of the target network. In this paper, we propose an automatic approach for constructing network models that are adapted to the desired network features. We employ Genetic Algorithms in order to evolve network models based on the characteristics of the target networks. The experimental evaluations show that our proposed framework, called NetMix, results network models that outperform baseline models according to the compliance with the desired features of the target networks.