A Mutual Attraction Model for Both Assortative and Disassortative Weighted Networks

TL;DR

This paper introduces a Mutual Attraction Model for weighted networks that captures both assortative and disassortative structures, reproduces scale-free distributions, and allows tunable clustering and degree correlations.

Contribution

The model unifies the characterization of both assortative and disassortative weighted networks through mutual attraction mechanisms.

Findings

Reproduces scale-free distributions of degree, weight, and strength.

Achieves tunable clustering coefficient and degree assortativity.

First model to unify both assortative and disassortative weighted networks.

Abstract

In most networks, the connection between a pair of nodes is the result of their mutual affinity and attachment. In this letter, we will propose a Mutual Attraction Model to characterize weighted evolving networks. By introducing the initial attractiveness $A$ and the general mechanism of mutual attraction (controlled by parameter $m$), the model can naturally reproduce scale-free distributions of degree, weight and strength, as found in many real systems. Simulation results are in consistent with theoretical predictions. Interestingly, we also obtain nontrivial clustering coefficient C and tunable degree assortativity r, depending on $m$ and A. Our weighted model appears as the first one that unifies the characterization of both assortative and disassortative weighted networks.